Overview

Dataset statistics

Number of variables49
Number of observations1192757
Missing cells1192757
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 GiB
Average record size in memory1.3 KiB

Variable types

Categorical37
Numeric12

Alerts

settlementdate has a high cardinality: 910 distinct valuesHigh cardinality
BMU ID has a high cardinality: 483 distinct valuesHigh cardinality
BMU Party ID has a high cardinality: 133 distinct valuesHigh cardinality
BMU Party Name has a high cardinality: 134 distinct valuesHigh cardinality
acceptedprice is highly overall correlated with acceptedvolume and 1 other fieldsHigh correlation
acceptedvolume is highly overall correlated with acceptedprice and 1 other fieldsHigh correlation
LOC LAT is highly overall correlated with acceptedprice and 27 other fieldsHigh correlation
LOC LONG is highly overall correlated with LOC Center LAT and 14 other fieldsHigh correlation
LOC Center LAT is highly overall correlated with LOC LAT and 25 other fieldsHigh correlation
LOC Center LONG is highly overall correlated with LOC LAT and 22 other fieldsHigh correlation
Transmission Loss Factor is highly overall correlated with LOC LAT and 24 other fieldsHigh correlation
Generation Capacity is highly overall correlated with Demand Capacity and 4 other fieldsHigh correlation
Demand Capacity is highly overall correlated with Generation Capacity and 1 other fieldsHigh correlation
recordtype is highly overall correlated with acceptedvolume and 1 other fieldsHigh correlation
BMU Type is highly overall correlated with PC Flag and 3 other fieldsHigh correlation
BMU Fuel Type is highly overall correlated with recordtype and 9 other fieldsHigh correlation
BMU GSP Group Id is highly overall correlated with LOC LAT and 25 other fieldsHigh correlation
BMU GSP Group Name is highly overall correlated with LOC LAT and 25 other fieldsHigh correlation
GSP LOC Center is highly overall correlated with LOC LAT and 25 other fieldsHigh correlation
BZONE is highly overall correlated with LOC LAT and 28 other fieldsHigh correlation
BZONE GENERATION is highly overall correlated with LOC LAT and 23 other fieldsHigh correlation
BZONE DEMAND is highly overall correlated with LOC LAT and 23 other fieldsHigh correlation
Z1 is highly overall correlated with LOC LAT and 6 other fieldsHigh correlation
Z2 is highly overall correlated with LOC LAT and 14 other fieldsHigh correlation
Z3 is highly overall correlated with LOC LAT and 12 other fieldsHigh correlation
Z4 is highly overall correlated with LOC LAT and 7 other fieldsHigh correlation
Z5 is highly overall correlated with LOC LAT and 10 other fieldsHigh correlation
Z6 is highly overall correlated with LOC LAT and 14 other fieldsHigh correlation
Z7 is highly overall correlated with LOC LAT and 15 other fieldsHigh correlation
Z8 is highly overall correlated with LOC LAT and 16 other fieldsHigh correlation
Z9 is highly overall correlated with LOC LAT and 17 other fieldsHigh correlation
Z10 is highly overall correlated with LOC LAT and 17 other fieldsHigh correlation
Z11 is highly overall correlated with LOC LAT and 14 other fieldsHigh correlation
Z12 is highly overall correlated with LOC LAT and 15 other fieldsHigh correlation
Z13 is highly overall correlated with LOC LAT and 14 other fieldsHigh correlation
Z14 is highly overall correlated with LOC LAT and 16 other fieldsHigh correlation
Z15 is highly overall correlated with LOC LAT and 11 other fieldsHigh correlation
Z16 is highly overall correlated with LOC LAT and 14 other fieldsHigh correlation
Z17 is highly overall correlated with LOC LAT and 14 other fieldsHigh correlation
Trading Unit is highly overall correlated with LOC LAT and 30 other fieldsHigh correlation
PC Flag is highly overall correlated with BMU Type and 3 other fieldsHigh correlation
PC Status is highly overall correlated with Generation Capacity and 5 other fieldsHigh correlation
Exempt Export Flag is highly overall correlated with BMU Type and 3 other fieldsHigh correlation
Base TU Flag is highly overall correlated with Generation Capacity and 6 other fieldsHigh correlation
BMU Type is highly imbalanced (53.9%)Imbalance
Z1 is highly imbalanced (79.0%)Imbalance
Z3 is highly imbalanced (61.8%)Imbalance
Z4 is highly imbalanced (57.5%)Imbalance
Z5 is highly imbalanced (63.0%)Imbalance
Z6 is highly imbalanced (56.0%)Imbalance
Z13 is highly imbalanced (51.8%)Imbalance
Z14 is highly imbalanced (58.9%)Imbalance
Z15 is highly imbalanced (65.0%)Imbalance
PC Flag is highly imbalanced (56.1%)Imbalance
FPN Flag is highly imbalanced (95.5%)Imbalance
Z1 has 183824 (15.4%) missing valuesMissing
Z2 has 14698 (1.2%) missing valuesMissing
Z3 has 12790 (1.1%) missing valuesMissing
Z4 has 36214 (3.0%) missing valuesMissing
Z5 has 32102 (2.7%) missing valuesMissing
Z6 has 91707 (7.7%) missing valuesMissing
Z7 has 13798 (1.2%) missing valuesMissing
Z8 has 138370 (11.6%) missing valuesMissing
Z9 has 188279 (15.8%) missing valuesMissing
Z10 has 105795 (8.9%) missing valuesMissing
Z11 has 38014 (3.2%) missing valuesMissing
Z12 has 52887 (4.4%) missing valuesMissing
Z13 has 117165 (9.8%) missing valuesMissing
Z14 has 18845 (1.6%) missing valuesMissing
Z15 has 69609 (5.8%) missing valuesMissing
Z16 has 47306 (4.0%) missing valuesMissing
Z17 has 31354 (2.6%) missing valuesMissing
acceptedprice is highly skewed (γ1 = 104.8295676)Skewed
acceptedprice has 60889 (5.1%) zerosZeros
Generation Capacity has 26692 (2.2%) zerosZeros
Demand Capacity has 231544 (19.4%) zerosZeros

Reproduction

Analysis started2023-07-09 00:05:13.169985
Analysis finished2023-07-09 00:10:43.166978
Duration5 minutes and 30 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

recordtype
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.1 MiB
BID
745990 
OFFER
446767 

Length

Max length5
Median length3
Mean length3.7491333
Min length3

Characters and Unicode

Total characters4471805
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBID
2nd rowBID
3rd rowOFFER
4th rowBID
5th rowOFFER

Common Values

ValueCountFrequency (%)
BID 745990
62.5%
OFFER 446767
37.5%

Length

2023-07-09T00:10:43.294128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:43.543632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bid 745990
62.5%
offer 446767
37.5%

Most occurring characters

ValueCountFrequency (%)
F 893534
20.0%
B 745990
16.7%
I 745990
16.7%
D 745990
16.7%
O 446767
10.0%
E 446767
10.0%
R 446767
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4471805
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 893534
20.0%
B 745990
16.7%
I 745990
16.7%
D 745990
16.7%
O 446767
10.0%
E 446767
10.0%
R 446767
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4471805
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 893534
20.0%
B 745990
16.7%
I 745990
16.7%
D 745990
16.7%
O 446767
10.0%
E 446767
10.0%
R 446767
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4471805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 893534
20.0%
B 745990
16.7%
I 745990
16.7%
D 745990
16.7%
O 446767
10.0%
E 446767
10.0%
R 446767
10.0%

settlementdate
Categorical

Distinct910
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.2 MiB
2023-04-12
 
4499
2022-06-11
 
4431
2022-10-06
 
4337
2023-04-11
 
4226
2022-11-10
 
3968
Other values (905)
1171296 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11927570
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-01
2nd row2021-01-01
3rd row2021-01-01
4th row2021-01-01
5th row2021-01-01

Common Values

ValueCountFrequency (%)
2023-04-12 4499
 
0.4%
2022-06-11 4431
 
0.4%
2022-10-06 4337
 
0.4%
2023-04-11 4226
 
0.4%
2022-11-10 3968
 
0.3%
2023-04-10 3634
 
0.3%
2023-03-22 3611
 
0.3%
2023-05-04 3593
 
0.3%
2022-06-12 3547
 
0.3%
2021-11-18 3538
 
0.3%
Other values (900) 1153373
96.7%

Length

2023-07-09T00:10:43.723023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-04-12 4499
 
0.4%
2022-06-11 4431
 
0.4%
2022-10-06 4337
 
0.4%
2023-04-11 4226
 
0.4%
2022-11-10 3968
 
0.3%
2023-04-10 3634
 
0.3%
2023-03-22 3611
 
0.3%
2023-05-04 3593
 
0.3%
2022-06-12 3547
 
0.3%
2021-11-18 3538
 
0.3%
Other values (900) 1153373
96.7%

Most occurring characters

ValueCountFrequency (%)
2 3584986
30.1%
0 2673264
22.4%
- 2385514
20.0%
1 1536702
12.9%
3 517613
 
4.3%
6 229850
 
1.9%
4 224283
 
1.9%
5 216290
 
1.8%
9 190901
 
1.6%
7 185027
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9542056
80.0%
Dash Punctuation 2385514
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3584986
37.6%
0 2673264
28.0%
1 1536702
16.1%
3 517613
 
5.4%
6 229850
 
2.4%
4 224283
 
2.4%
5 216290
 
2.3%
9 190901
 
2.0%
7 185027
 
1.9%
8 183140
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 2385514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11927570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3584986
30.1%
0 2673264
22.4%
- 2385514
20.0%
1 1536702
12.9%
3 517613
 
4.3%
6 229850
 
1.9%
4 224283
 
1.9%
5 216290
 
1.8%
9 190901
 
1.6%
7 185027
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11927570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3584986
30.1%
0 2673264
22.4%
- 2385514
20.0%
1 1536702
12.9%
3 517613
 
4.3%
6 229850
 
1.9%
4 224283
 
1.9%
5 216290
 
1.8%
9 190901
 
1.6%
7 185027
 
1.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.4 MiB
2022
484465 
2021
474003 
2023
234289 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4771028
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2022 484465
40.6%
2021 474003
39.7%
2023 234289
19.6%

Length

2023-07-09T00:10:43.924963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:44.136667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2022 484465
40.6%
2021 474003
39.7%
2023 234289
19.6%

Most occurring characters

ValueCountFrequency (%)
2 2869979
60.2%
0 1192757
25.0%
1 474003
 
9.9%
3 234289
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4771028
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2869979
60.2%
0 1192757
25.0%
1 474003
 
9.9%
3 234289
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4771028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2869979
60.2%
0 1192757
25.0%
1 474003
 
9.9%
3 234289
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4771028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2869979
60.2%
0 1192757
25.0%
1 474003
 
9.9%
3 234289
 
4.9%

settlementdate_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0252516
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 MiB
2023-07-09T00:10:44.307410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5420101
Coefficient of variation (CV)0.58786094
Kurtosis-1.2674722
Mean6.0252516
Median Absolute Deviation (MAD)3
Skewness0.19436087
Sum7186661
Variance12.545835
MonotonicityNot monotonic
2023-07-09T00:10:44.493914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 136503
11.4%
1 123805
10.4%
3 111274
9.3%
6 107966
9.1%
4 107541
9.0%
11 106836
9.0%
5 101641
8.5%
10 98299
8.2%
12 80393
6.7%
8 73766
6.2%
Other values (2) 144733
12.1%
ValueCountFrequency (%)
1 123805
10.4%
2 136503
11.4%
3 111274
9.3%
4 107541
9.0%
5 101641
8.5%
6 107966
9.1%
7 72882
6.1%
8 73766
6.2%
9 71851
6.0%
10 98299
8.2%
ValueCountFrequency (%)
12 80393
6.7%
11 106836
9.0%
10 98299
8.2%
9 71851
6.0%
8 73766
6.2%
7 72882
6.1%
6 107966
9.1%
5 101641
8.5%
4 107541
9.0%
3 111274
9.3%

settlementdate_day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.575868
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 MiB
2023-07-09T00:10:44.721129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6534231
Coefficient of variation (CV)0.55556601
Kurtosis-1.1733418
Mean15.575868
Median Absolute Deviation (MAD)7
Skewness0.03852104
Sum18578226
Variance74.881731
MonotonicityNot monotonic
2023-07-09T00:10:44.932111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 49095
 
4.1%
10 45936
 
3.9%
9 44364
 
3.7%
25 43945
 
3.7%
6 43236
 
3.6%
12 43083
 
3.6%
20 42534
 
3.6%
19 41454
 
3.5%
24 40985
 
3.4%
23 40557
 
3.4%
Other values (21) 757568
63.5%
ValueCountFrequency (%)
1 35432
3.0%
2 36250
3.0%
3 38704
3.2%
4 40045
3.4%
5 36499
3.1%
6 43236
3.6%
7 37909
3.2%
8 38893
3.3%
9 44364
3.7%
10 45936
3.9%
ValueCountFrequency (%)
31 20913
1.8%
30 35177
2.9%
29 33232
2.8%
28 31187
2.6%
27 35097
2.9%
26 39475
3.3%
25 43945
3.7%
24 40985
3.4%
23 40557
3.4%
22 37233
3.1%

settlementperiod
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.328264
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 MiB
2023-07-09T00:10:45.178819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median25
Q337
95-th percentile46
Maximum48
Range47
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.462702
Coefficient of variation (CV)0.53152881
Kurtosis-1.1537917
Mean25.328264
Median Absolute Deviation (MAD)11
Skewness-0.066646673
Sum30210464
Variance181.24434
MonotonicityNot monotonic
2023-07-09T00:10:45.445281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15 31499
 
2.6%
16 29412
 
2.5%
34 29329
 
2.5%
14 29269
 
2.5%
17 29130
 
2.4%
35 29099
 
2.4%
39 28850
 
2.4%
38 28496
 
2.4%
36 28330
 
2.4%
37 28247
 
2.4%
Other values (38) 901096
75.5%
ValueCountFrequency (%)
1 22050
1.8%
2 21102
1.8%
3 20438
1.7%
4 19878
1.7%
5 19500
1.6%
6 19151
1.6%
7 19208
1.6%
8 19029
1.6%
9 19509
1.6%
10 20688
1.7%
ValueCountFrequency (%)
48 21710
1.8%
47 24851
2.1%
46 24303
2.0%
45 24315
2.0%
44 25051
2.1%
43 25925
2.2%
42 26048
2.2%
41 26965
2.3%
40 27727
2.3%
39 28850
2.4%

BMU ID
Categorical

Distinct483
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.9 MiB
T_MRWD-1
 
25738
T_GRAI-8
 
21464
T_WBURB-2
 
21015
T_WBURB-1
 
19648
T_CARR-2
 
18864
Other values (478)
1086028 

Length

Max length11
Median length10
Mean length8.8077949
Min length7

Characters and Unicode

Total characters10505559
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE_GYAR-1
2nd rowE_SHOS-1
3rd rowT_CARR-2
4th rowT_CDCL-1
5th rowT_CDCL-1

Common Values

ValueCountFrequency (%)
T_MRWD-1 25738
 
2.2%
T_GRAI-8 21464
 
1.8%
T_WBURB-2 21015
 
1.8%
T_WBURB-1 19648
 
1.6%
T_CARR-2 18864
 
1.6%
T_CDCL-1 18620
 
1.6%
T_CARR-1 17310
 
1.5%
T_SEAB-1 16155
 
1.4%
T_SHBA-1 16096
 
1.3%
T_WBURB-3 15654
 
1.3%
Other values (473) 1002193
84.0%

Length

2023-07-09T00:10:45.705492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
t_mrwd-1 25738
 
2.2%
t_grai-8 21464
 
1.8%
t_wburb-2 21015
 
1.8%
t_wburb-1 19648
 
1.6%
t_carr-2 18864
 
1.6%
t_cdcl-1 18620
 
1.6%
t_carr-1 17310
 
1.5%
t_seab-1 16155
 
1.4%
t_shba-1 16096
 
1.3%
t_wburb-3 15654
 
1.3%
Other values (473) 1002193
84.0%

Most occurring characters

ValueCountFrequency (%)
_ 1345672
 
12.8%
T 1122967
 
10.7%
- 1012458
 
9.6%
1 703474
 
6.7%
A 527762
 
5.0%
R 510260
 
4.9%
E 481799
 
4.6%
S 380913
 
3.6%
2 371477
 
3.5%
W 346008
 
3.3%
Other values (28) 3702769
35.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6478164
61.7%
Decimal Number 1669265
 
15.9%
Connector Punctuation 1345672
 
12.8%
Dash Punctuation 1012458
 
9.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1122967
17.3%
A 527762
 
8.1%
R 510260
 
7.9%
E 481799
 
7.4%
S 380913
 
5.9%
W 346008
 
5.3%
B 345003
 
5.3%
C 329500
 
5.1%
D 309621
 
4.8%
L 289936
 
4.5%
Other values (16) 1834395
28.3%
Decimal Number
ValueCountFrequency (%)
1 703474
42.1%
2 371477
22.3%
0 310173
18.6%
3 123650
 
7.4%
4 61183
 
3.7%
6 33186
 
2.0%
5 32673
 
2.0%
8 21719
 
1.3%
7 11713
 
0.7%
9 17
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1345672
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1012458
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6478164
61.7%
Common 4027395
38.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1122967
17.3%
A 527762
 
8.1%
R 510260
 
7.9%
E 481799
 
7.4%
S 380913
 
5.9%
W 346008
 
5.3%
B 345003
 
5.3%
C 329500
 
5.1%
D 309621
 
4.8%
L 289936
 
4.5%
Other values (16) 1834395
28.3%
Common
ValueCountFrequency (%)
_ 1345672
33.4%
- 1012458
25.1%
1 703474
17.5%
2 371477
 
9.2%
0 310173
 
7.7%
3 123650
 
3.1%
4 61183
 
1.5%
6 33186
 
0.8%
5 32673
 
0.8%
8 21719
 
0.5%
Other values (2) 11730
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10505559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1345672
 
12.8%
T 1122967
 
10.7%
- 1012458
 
9.6%
1 703474
 
6.7%
A 527762
 
5.0%
R 510260
 
4.9%
E 481799
 
4.6%
S 380913
 
3.6%
2 371477
 
3.5%
W 346008
 
3.3%
Other values (28) 3702769
35.2%

acceptedprice
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct27936
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.743841
Minimum-9999
Maximum99999
Zeros60889
Zeros (%)5.1%
Negative264369
Negative (%)22.2%
Memory size9.1 MiB
2023-07-09T00:10:45.969828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile-75
Q10
median85
Q3166.9
95-th percentile329.594
Maximum99999
Range109998
Interquartile range (IQR)166.9

Descriptive statistics

Standard deviation204.68774
Coefficient of variation (CV)2.0521342
Kurtosis47866.904
Mean99.743841
Median Absolute Deviation (MAD)85
Skewness104.82957
Sum1.1897016 × 108
Variance41897.073
MonotonicityNot monotonic
2023-07-09T00:10:46.222944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60889
 
5.1%
-56.58 10386
 
0.9%
-72 10312
 
0.9%
50 9018
 
0.8%
-75 8745
 
0.7%
150 8595
 
0.7%
-67.13 8219
 
0.7%
100 7787
 
0.7%
200 7531
 
0.6%
-69.49 7287
 
0.6%
Other values (27926) 1053988
88.4%
ValueCountFrequency (%)
-9999 10
< 0.1%
-5491.18 1
 
< 0.1%
-5024.5 1
 
< 0.1%
-3203.253333 1
 
< 0.1%
-999 2
 
< 0.1%
-500 7
< 0.1%
-285 15
< 0.1%
-252.54 1
 
< 0.1%
-250 5
 
< 0.1%
-221.31 1
 
< 0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
10000 2
 
< 0.1%
9999 34
< 0.1%
8000 1
 
< 0.1%
6000 2
 
< 0.1%
5500 2
 
< 0.1%
5234.11 1
 
< 0.1%
5000 4
 
< 0.1%
4991.5 1
 
< 0.1%
4950 3
 
< 0.1%

acceptedvolume
Real number (ℝ)

Distinct161808
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0556221
Minimum-1220.238
Maximum1100
Zeros7389
Zeros (%)0.6%
Negative741356
Negative (%)62.2%
Memory size9.1 MiB
2023-07-09T00:10:46.477817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1220.238
5-th percentile-162
Q1-52.001
median-10.536
Q312.1
95-th percentile258
Maximum1100
Range2320.238
Interquartile range (IQR)64.101

Descriptive statistics

Standard deviation142.63239
Coefficient of variation (CV)-135.1169
Kurtosis13.037221
Mean-1.0556221
Median Absolute Deviation (MAD)30.536
Skewness-0.042919835
Sum-1259100.7
Variance20343.998
MonotonicityNot monotonic
2023-07-09T00:10:46.732040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 10017
 
0.8%
20 9281
 
0.8%
-25 8660
 
0.7%
-15 7906
 
0.7%
0 7389
 
0.6%
-20 7314
 
0.6%
16 5987
 
0.5%
-40 5663
 
0.5%
-24 5467
 
0.5%
18 5033
 
0.4%
Other values (161798) 1120040
93.9%
ValueCountFrequency (%)
-1220.238 1
 
< 0.1%
-1195.762 1
 
< 0.1%
-1180 268
< 0.1%
-1179.984 2
 
< 0.1%
-1179.95 1
 
< 0.1%
-1179.016 3
 
< 0.1%
-1179 21
 
< 0.1%
-1178.984 1
 
< 0.1%
-1178.958 1
 
< 0.1%
-1178.934 1
 
< 0.1%
ValueCountFrequency (%)
1100 1
 
< 0.1%
1066.934 1
 
< 0.1%
1009.334 1
 
< 0.1%
925.832 1
 
< 0.1%
922.98 1
 
< 0.1%
918.316 1
 
< 0.1%
897.334 1
 
< 0.1%
890.334 1
 
< 0.1%
889 1
 
< 0.1%
888 6
< 0.1%

BMU Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.0 MiB
T
893177 
E
134832 
2
134270 
V
 
16465
M
 
11833

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1192757
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowT
4th rowT
5th rowT

Common Values

ValueCountFrequency (%)
T 893177
74.9%
E 134832
 
11.3%
2 134270
 
11.3%
V 16465
 
1.4%
M 11833
 
1.0%
C 2180
 
0.2%

Length

2023-07-09T00:10:46.958947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:47.189615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
t 893177
74.9%
e 134832
 
11.3%
2 134270
 
11.3%
v 16465
 
1.4%
m 11833
 
1.0%
c 2180
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 893177
74.9%
E 134832
 
11.3%
2 134270
 
11.3%
V 16465
 
1.4%
M 11833
 
1.0%
C 2180
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1058487
88.7%
Decimal Number 134270
 
11.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 893177
84.4%
E 134832
 
12.7%
V 16465
 
1.6%
M 11833
 
1.1%
C 2180
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 134270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1058487
88.7%
Common 134270
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 893177
84.4%
E 134832
 
12.7%
V 16465
 
1.6%
M 11833
 
1.1%
C 2180
 
0.2%
Common
ValueCountFrequency (%)
2 134270
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1192757
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 893177
74.9%
E 134832
 
11.3%
2 134270
 
11.3%
V 16465
 
1.4%
M 11833
 
1.0%
C 2180
 
0.2%

BMU Fuel Type
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.7 MiB
CCGT
530340 
WIND
250430 
OTHER
163093 
PS
84228 
NPSHYD
78263 
Other values (4)
86403 

Length

Max length7
Median length4
Mean length4.2602601
Min length2

Characters and Unicode

Total characters5081455
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCCGT
2nd rowCCGT
3rd rowCCGT
4th rowCCGT
5th rowCCGT

Common Values

ValueCountFrequency (%)
CCGT 530340
44.5%
WIND 250430
21.0%
OTHER 163093
 
13.7%
PS 84228
 
7.1%
NPSHYD 78263
 
6.6%
BATTERY 30743
 
2.6%
COAL 22660
 
1.9%
BIOMASS 22345
 
1.9%
OCGT 10655
 
0.9%

Length

2023-07-09T00:10:47.400852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:47.668386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ccgt 530340
44.5%
wind 250430
21.0%
other 163093
 
13.7%
ps 84228
 
7.1%
npshyd 78263
 
6.6%
battery 30743
 
2.6%
coal 22660
 
1.9%
biomass 22345
 
1.9%
ocgt 10655
 
0.9%

Most occurring characters

ValueCountFrequency (%)
C 1093995
21.5%
T 765574
15.1%
G 540995
10.6%
N 328693
 
6.5%
D 328693
 
6.5%
I 272775
 
5.4%
W 250430
 
4.9%
H 241356
 
4.7%
O 218753
 
4.3%
S 207181
 
4.1%
Other values (8) 833010
16.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5081455
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 1093995
21.5%
T 765574
15.1%
G 540995
10.6%
N 328693
 
6.5%
D 328693
 
6.5%
I 272775
 
5.4%
W 250430
 
4.9%
H 241356
 
4.7%
O 218753
 
4.3%
S 207181
 
4.1%
Other values (8) 833010
16.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5081455
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 1093995
21.5%
T 765574
15.1%
G 540995
10.6%
N 328693
 
6.5%
D 328693
 
6.5%
I 272775
 
5.4%
W 250430
 
4.9%
H 241356
 
4.7%
O 218753
 
4.3%
S 207181
 
4.1%
Other values (8) 833010
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5081455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 1093995
21.5%
T 765574
15.1%
G 540995
10.6%
N 328693
 
6.5%
D 328693
 
6.5%
I 272775
 
5.4%
W 250430
 
4.9%
H 241356
 
4.7%
O 218753
 
4.3%
S 207181
 
4.1%
Other values (8) 833010
16.4%

BMU GSP Group Id
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.1 MiB
_P
296996 
_M
177994 
_D
102343 
_B
99029 
_N
89394 
Other values (9)
427001 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2385514
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row_A
2nd row_J
3rd row_G
4th row_B
5th row_B

Common Values

ValueCountFrequency (%)
_P 296996
24.9%
_M 177994
14.9%
_D 102343
 
8.6%
_B 99029
 
8.3%
_N 89394
 
7.5%
_J 86300
 
7.2%
_G 80095
 
6.7%
_H 62734
 
5.3%
_K 60395
 
5.1%
_L 54699
 
4.6%
Other values (4) 82778
 
6.9%

Length

2023-07-09T00:10:47.902091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p 296996
24.9%
m 177994
14.9%
d 102343
 
8.6%
b 99029
 
8.3%
n 89394
 
7.5%
j 86300
 
7.2%
g 80095
 
6.7%
h 62734
 
5.3%
k 60395
 
5.1%
l 54699
 
4.6%
Other values (4) 82778
 
6.9%

Most occurring characters

ValueCountFrequency (%)
_ 1192757
50.0%
P 296996
 
12.4%
M 177994
 
7.5%
D 102343
 
4.3%
B 99029
 
4.2%
N 89394
 
3.7%
J 86300
 
3.6%
G 80095
 
3.4%
H 62734
 
2.6%
K 60395
 
2.5%
Other values (5) 137477
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Connector Punctuation 1192757
50.0%
Uppercase Letter 1192757
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 296996
24.9%
M 177994
14.9%
D 102343
 
8.6%
B 99029
 
8.3%
N 89394
 
7.5%
J 86300
 
7.2%
G 80095
 
6.7%
H 62734
 
5.3%
K 60395
 
5.1%
L 54699
 
4.6%
Other values (4) 82778
 
6.9%
Connector Punctuation
ValueCountFrequency (%)
_ 1192757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1192757
50.0%
Latin 1192757
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 296996
24.9%
M 177994
14.9%
D 102343
 
8.6%
B 99029
 
8.3%
N 89394
 
7.5%
J 86300
 
7.2%
G 80095
 
6.7%
H 62734
 
5.3%
K 60395
 
5.1%
L 54699
 
4.6%
Other values (4) 82778
 
6.9%
Common
ValueCountFrequency (%)
_ 1192757
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2385514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1192757
50.0%
P 296996
 
12.4%
M 177994
 
7.5%
D 102343
 
4.3%
B 99029
 
4.2%
N 89394
 
3.7%
J 86300
 
3.6%
G 80095
 
3.4%
H 62734
 
2.6%
K 60395
 
2.5%
Other values (5) 137477
 
5.8%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.0 MiB
Northern Scotland
297032 
Yorkshire
177994 
Merseyside and Northern Wales
102343 
East Midlands
99029 
Southern Scotland
89394 
Other values (9)
426965 

Length

Max length29
Median length21
Mean length16.804621
Min length6

Characters and Unicode

Total characters20043829
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern England
2nd rowSouth Eastern England
3rd rowNorth Western England
4th rowEast Midlands
5th rowEast Midlands

Common Values

ValueCountFrequency (%)
Northern Scotland 297032
24.9%
Yorkshire 177994
14.9%
Merseyside and Northern Wales 102343
 
8.6%
East Midlands 99029
 
8.3%
Southern Scotland 89394
 
7.5%
South Eastern England 86300
 
7.2%
North Western England 80095
 
6.7%
Southern England 62734
 
5.3%
Southern Wales 60395
 
5.1%
South Western England 54663
 
4.6%
Other values (4) 82778
 
6.9%

Length

2023-07-09T00:10:48.130528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
northern 399375
15.2%
scotland 386426
14.7%
england 337538
12.9%
southern 212523
8.1%
yorkshire 177994
6.8%
wales 162738
 
6.2%
south 140963
 
5.4%
eastern 140046
 
5.3%
western 134758
 
5.1%
midlands 115124
 
4.4%
Other values (6) 418305
15.9%

Most occurring characters

ValueCountFrequency (%)
n 2191545
10.9%
r 1829966
 
9.1%
e 1685316
 
8.4%
t 1614773
 
8.1%
1433033
 
7.1%
o 1428713
 
7.1%
a 1343244
 
6.7%
d 1171835
 
5.8%
s 1050470
 
5.2%
h 1016413
 
5.1%
Other values (14) 5278521
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16087349
80.3%
Uppercase Letter 2523447
 
12.6%
Space Separator 1433033
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2191545
13.6%
r 1829966
11.4%
e 1685316
10.5%
t 1614773
10.0%
o 1428713
8.9%
a 1343244
8.3%
d 1171835
7.3%
s 1050470
6.5%
h 1016413
6.3%
l 1001826
6.2%
Other values (6) 1753248
10.9%
Uppercase Letter
ValueCountFrequency (%)
S 739912
29.3%
E 576613
22.9%
N 484933
19.2%
W 313591
12.4%
M 217467
 
8.6%
Y 177994
 
7.1%
L 12937
 
0.5%
Space Separator
ValueCountFrequency (%)
1433033
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18610796
92.9%
Common 1433033
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2191545
11.8%
r 1829966
9.8%
e 1685316
 
9.1%
t 1614773
 
8.7%
o 1428713
 
7.7%
a 1343244
 
7.2%
d 1171835
 
6.3%
s 1050470
 
5.6%
h 1016413
 
5.5%
l 1001826
 
5.4%
Other values (13) 4276695
23.0%
Common
ValueCountFrequency (%)
1433033
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20043829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2191545
10.9%
r 1829966
 
9.1%
e 1685316
 
8.4%
t 1614773
 
8.1%
1433033
 
7.1%
o 1428713
 
7.1%
a 1343244
 
6.7%
d 1171835
 
5.8%
s 1050470
 
5.2%
h 1016413
 
5.1%
Other values (14) 5278521
26.3%

LOC LAT
Real number (ℝ)

Distinct256
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.001453
Minimum50.388495
Maximum58.895244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 MiB
2023-07-09T00:10:48.415338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.388495
5-th percentile50.898831
Q152.203604
median53.403787
Q355.907521
95-th percentile58.077384
Maximum58.895244
Range8.5067489
Interquartile range (IQR)3.7039169

Descriptive statistics

Standard deviation2.2551209
Coefficient of variation (CV)0.041760375
Kurtosis-0.91718542
Mean54.001453
Median Absolute Deviation (MAD)1.7408097
Skewness0.49608832
Sum64410611
Variance5.0855703
MonotonicityNot monotonic
2023-07-09T00:10:48.686718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.36027054 57364
 
4.8%
51.44354566 46527
 
3.9%
51.68300308 41024
 
3.4%
53.43695862 36174
 
3.0%
53.11937787 33650
 
2.8%
54.12746357 32211
 
2.7%
53.07515403 31275
 
2.6%
53.23161426 31253
 
2.6%
53.7351875 29543
 
2.5%
50.89883051 25738
 
2.2%
Other values (246) 827998
69.4%
ValueCountFrequency (%)
50.38849516 12920
1.1%
50.39684184 44
 
< 0.1%
50.59074232 137
 
< 0.1%
50.62314342 373
 
< 0.1%
50.70742187 37
 
< 0.1%
50.726363 1014
 
0.1%
50.73518555 12176
1.0%
50.73518555 23
 
< 0.1%
50.74625917 36
 
< 0.1%
50.82951064 10915
0.9%
ValueCountFrequency (%)
58.89524403 6924
 
0.6%
58.568706 4635
 
0.4%
58.51031322 7259
0.6%
58.447304 10
 
< 0.1%
58.44069613 18020
1.5%
58.43441752 5085
 
0.4%
58.40769912 58
 
< 0.1%
58.35706796 84
 
< 0.1%
58.35051379 1077
 
0.1%
58.21067822 3
 
< 0.1%

LOC LONG
Real number (ℝ)

Distinct256
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.3626586
Minimum-6.4179968
Maximum2.2416223
Zeros0
Zeros (%)0.0%
Negative1080288
Negative (%)90.6%
Memory size9.1 MiB
2023-07-09T00:10:48.957211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.4179968
5-th percentile-4.9948654
Q1-3.9688246
median-2.670153
Q3-0.81350495
95-th percentile0.69090647
Maximum2.2416223
Range8.6596191
Interquartile range (IQR)3.1553197

Descriptive statistics

Standard deviation1.8228538
Coefficient of variation (CV)-0.77152651
Kurtosis-1.1560741
Mean-2.3626586
Median Absolute Deviation (MAD)1.4891014
Skewness0.12893157
Sum-2818077.6
Variance3.3227959
MonotonicityNot monotonic
2023-07-09T00:10:49.210430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8135049481 57364
 
4.8%
0.7077745465 46527
 
3.9%
-4.994865371 41024
 
3.4%
-2.408211725 36174
 
3.0%
-4.113995377 33650
 
2.8%
-2.769975155 32211
 
2.7%
-0.8561335864 31275
 
2.6%
-3.081947146 31253
 
2.6%
-0.2432812093 29543
 
2.5%
-1.437187376 25738
 
2.2%
Other values (246) 827998
69.4%
ValueCountFrequency (%)
-6.417996762 5227
0.4%
-5.624398433 5
 
< 0.1%
-5.622701519 616
 
0.1%
-5.601716691 137
 
< 0.1%
-5.576595803 4310
0.4%
-5.463966049 36
 
< 0.1%
-5.281216 2
 
< 0.1%
-5.239121201 190
 
< 0.1%
-5.219652997 20
 
< 0.1%
-5.211471776 2791
0.2%
ValueCountFrequency (%)
2.241622324 1495
0.1%
2.04 141
 
< 0.1%
1.91735627 142
 
< 0.1%
1.733725011 2934
0.2%
1.6334 122
 
< 0.1%
1.5927 98
 
< 0.1%
1.395866736 10
 
< 0.1%
1.36 168
 
< 0.1%
1.169993728 347
 
< 0.1%
1.1464 36
 
< 0.1%

GSP LOC Center
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.1 MiB
Kingussie
297032 
Pollington
177994 
Corwen
102343 
Ab Kettleby
99029 
Leadhills
89394 
Other values (9)
426965 

Length

Max length12
Median length11
Mean length9.0120695
Min length6

Characters and Unicode

Total characters10749209
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDalham
2nd rowPembury
3rd rowCarnforth
4th rowAb Kettleby
5th rowAb Kettleby

Common Values

ValueCountFrequency (%)
Kingussie 297032
24.9%
Pollington 177994
14.9%
Corwen 102343
 
8.6%
Ab Kettleby 99029
 
8.3%
Leadhills 89394
 
7.5%
Pembury 86300
 
7.2%
Carnforth 80095
 
6.7%
Andover 62734
 
5.3%
Llanddeusant 60395
 
5.1%
South Tawton 54663
 
4.6%
Other values (4) 82778
 
6.9%

Length

2023-07-09T00:10:49.451872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kingussie 297032
21.7%
pollington 177994
13.0%
corwen 102343
 
7.5%
ab 99029
 
7.2%
kettleby 99029
 
7.2%
leadhills 89394
 
6.5%
pembury 86300
 
6.3%
carnforth 80095
 
5.9%
andover 62734
 
4.6%
llanddeusant 60395
 
4.4%
Other values (8) 213662
15.6%

Most occurring characters

ValueCountFrequency (%)
n 1079108
 
10.0%
e 922130
 
8.6%
i 895947
 
8.3%
l 806863
 
7.5%
s 756790
 
7.0%
o 744981
 
6.9%
t 636794
 
5.9%
u 511327
 
4.8%
g 475026
 
4.4%
a 463066
 
4.3%
Other values (21) 3457177
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9205952
85.6%
Uppercase Letter 1368007
 
12.7%
Space Separator 175250
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1079108
11.7%
e 922130
10.0%
i 895947
9.7%
l 806863
8.8%
s 756790
 
8.2%
o 744981
 
8.1%
t 636794
 
6.9%
u 511327
 
5.6%
g 475026
 
5.2%
a 463066
 
5.0%
Other values (10) 1913920
20.8%
Uppercase Letter
ValueCountFrequency (%)
K 396061
29.0%
P 269757
19.7%
C 198533
14.5%
L 162726
11.9%
A 161763
11.8%
S 54663
 
4.0%
T 54663
 
4.0%
D 48283
 
3.5%
H 16095
 
1.2%
W 5463
 
0.4%
Space Separator
ValueCountFrequency (%)
175250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10573959
98.4%
Common 175250
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1079108
 
10.2%
e 922130
 
8.7%
i 895947
 
8.5%
l 806863
 
7.6%
s 756790
 
7.2%
o 744981
 
7.0%
t 636794
 
6.0%
u 511327
 
4.8%
g 475026
 
4.5%
a 463066
 
4.4%
Other values (20) 3281927
31.0%
Common
ValueCountFrequency (%)
175250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10749209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1079108
 
10.0%
e 922130
 
8.6%
i 895947
 
8.3%
l 806863
 
7.5%
s 756790
 
7.0%
o 744981
 
6.9%
t 636794
 
5.9%
u 511327
 
4.8%
g 475026
 
4.4%
a 463066
 
4.3%
Other values (21) 3457177
32.2%

LOC Center LAT
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.958836
Minimum50.735186
Maximum57.229009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 MiB
2023-07-09T00:10:49.663936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.735186
5-th percentile51.148006
Q152.226649
median53.670497
Q355.469264
95-th percentile57.229009
Maximum57.229009
Range6.4938237
Interquartile range (IQR)3.2426149

Descriptive statistics

Standard deviation2.2197639
Coefficient of variation (CV)0.041138098
Kurtosis-1.2155067
Mean53.958836
Median Absolute Deviation (MAD)1.7987671
Skewness0.3338079
Sum64359780
Variance4.9273518
MonotonicityNot monotonic
2023-07-09T00:10:49.851341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
57.22900924 297032
24.9%
53.67049716 177994
14.9%
52.98011388 102343
 
8.6%
52.80004763 99029
 
8.3%
55.4692643 89394
 
7.5%
51.14800594 86300
 
7.2%
54.12746357 80095
 
6.7%
51.21098811 62734
 
5.3%
51.90578722 60395
 
5.1%
50.73518555 54663
 
4.6%
Other values (4) 82778
 
6.9%
ValueCountFrequency (%)
50.73518555 54663
 
4.6%
51.14800594 86300
7.2%
51.21098811 62734
 
5.3%
51.51108546 12937
 
1.1%
51.90578722 60395
 
5.1%
52.22664943 48283
 
4.0%
52.27969482 16095
 
1.3%
52.80004763 99029
8.3%
52.98011388 102343
8.6%
53.67049716 177994
14.9%
ValueCountFrequency (%)
57.22900924 297032
24.9%
55.4692643 89394
 
7.5%
54.66548534 5463
 
0.5%
54.12746357 80095
 
6.7%
53.67049716 177994
14.9%
52.98011388 102343
 
8.6%
52.80004763 99029
 
8.3%
52.27969482 16095
 
1.3%
52.22664943 48283
 
4.0%
51.90578722 60395
 
5.1%

LOC Center LONG
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4471239
Minimum-4.0788193
Maximum0.51999347
Zeros0
Zeros (%)0.0%
Negative1058174
Negative (%)88.7%
Memory size9.1 MiB
2023-07-09T00:10:50.064853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.0788193
5-th percentile-4.0788193
Q1-3.9096738
median-3.3692488
Q3-1.0712752
95-th percentile0.32783177
Maximum0.51999347
Range4.5988127
Interquartile range (IQR)2.8383986

Descriptive statistics

Standard deviation1.599773
Coefficient of variation (CV)-0.65373601
Kurtosis-1.2868998
Mean-2.4471239
Median Absolute Deviation (MAD)0.70957047
Skewness0.46620905
Sum-2918824.2
Variance2.5592737
MonotonicityNot monotonic
2023-07-09T00:10:50.255964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-4.078819251 297032
24.9%
-1.071275155 177994
14.9%
-3.369248779 102343
 
8.6%
-0.9277514146 99029
 
8.3%
-3.736325087 89394
 
7.5%
0.3278317727 86300
 
7.2%
-2.769975155 80095
 
6.7%
-1.493501823 62734
 
5.3%
-3.779420918 60395
 
5.1%
-3.909673771 54663
 
4.6%
Other values (4) 82778
 
6.9%
ValueCountFrequency (%)
-4.078819251 297032
24.9%
-3.909673771 54663
 
4.6%
-3.779420918 60395
 
5.1%
-3.736325087 89394
 
7.5%
-3.369248779 102343
 
8.6%
-2.769975155 80095
 
6.7%
-1.976215528 16095
 
1.3%
-1.737734077 5463
 
0.5%
-1.493501823 62734
 
5.3%
-1.071275155 177994
14.9%
ValueCountFrequency (%)
0.5199934698 48283
 
4.0%
0.3278317727 86300
7.2%
-0.03683875901 12937
 
1.1%
-0.9277514146 99029
8.3%
-1.071275155 177994
14.9%
-1.493501823 62734
 
5.3%
-1.737734077 5463
 
0.5%
-1.976215528 16095
 
1.3%
-2.769975155 80095
6.7%
-3.369248779 102343
8.6%

BZONE
Categorical

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.6 MiB
Z9
188279 
Z1
183824 
Z8
138370 
Z13
117165 
Z10
105795 
Other values (12)
459324 

Length

Max length3
Median length2
Mean length2.4032464
Min length2

Characters and Unicode

Total characters2866489
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZ12
2nd rowZ16
3rd rowZ9
4th rowZ10
5th rowZ10

Common Values

ValueCountFrequency (%)
Z9 188279
15.8%
Z1 183824
15.4%
Z8 138370
11.6%
Z13 117165
9.8%
Z10 105795
8.9%
Z6 91707
7.7%
Z15 69609
 
5.8%
Z12 52887
 
4.4%
Z16 47306
 
4.0%
Z11 38014
 
3.2%
Other values (7) 159801
13.4%

Length

2023-07-09T00:10:50.467611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
z9 188279
15.8%
z1 183824
15.4%
z8 138370
11.6%
z13 117165
9.8%
z10 105795
8.9%
z6 91707
7.7%
z15 69609
 
5.8%
z12 52887
 
4.4%
z16 47306
 
4.0%
z11 38014
 
3.2%
Other values (7) 159801
13.4%

Most occurring characters

ValueCountFrequency (%)
Z 1192757
41.6%
1 702813
24.5%
9 188279
 
6.6%
6 139013
 
4.8%
8 138370
 
4.8%
3 129955
 
4.5%
0 105795
 
3.7%
5 101711
 
3.5%
2 67585
 
2.4%
4 55059
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1673732
58.4%
Uppercase Letter 1192757
41.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 702813
42.0%
9 188279
 
11.2%
6 139013
 
8.3%
8 138370
 
8.3%
3 129955
 
7.8%
0 105795
 
6.3%
5 101711
 
6.1%
2 67585
 
4.0%
4 55059
 
3.3%
7 45152
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
Z 1192757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1673732
58.4%
Latin 1192757
41.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 702813
42.0%
9 188279
 
11.2%
6 139013
 
8.3%
8 138370
 
8.3%
3 129955
 
7.8%
0 105795
 
6.3%
5 101711
 
6.1%
2 67585
 
4.0%
4 55059
 
3.3%
7 45152
 
2.7%
Latin
ValueCountFrequency (%)
Z 1192757
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2866489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 1192757
41.6%
1 702813
24.5%
9 188279
 
6.6%
6 139013
 
4.8%
8 138370
 
4.8%
3 129955
 
4.5%
0 105795
 
3.7%
5 101711
 
3.5%
2 67585
 
2.4%
4 55059
 
1.9%

BZONE GENERATION
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.5 MiB
15000
371335 
14000
284279 
20000
257963 
17500
226293 
7500
52887 

Length

Max length5
Median length5
Mean length4.9556599
Min length4

Characters and Unicode

Total characters5910898
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7500
2nd row14000
3rd row17500
4th row20000
5th row20000

Common Values

ValueCountFrequency (%)
15000 371335
31.1%
14000 284279
23.8%
20000 257963
21.6%
17500 226293
19.0%
7500 52887
 
4.4%

Length

2023-07-09T00:10:50.721036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:50.988893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
15000 371335
31.1%
14000 284279
23.8%
20000 257963
21.6%
17500 226293
19.0%
7500 52887
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 3557054
60.2%
1 881907
 
14.9%
5 650515
 
11.0%
4 284279
 
4.8%
7 279180
 
4.7%
2 257963
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5910898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3557054
60.2%
1 881907
 
14.9%
5 650515
 
11.0%
4 284279
 
4.8%
7 279180
 
4.7%
2 257963
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5910898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3557054
60.2%
1 881907
 
14.9%
5 650515
 
11.0%
4 284279
 
4.8%
7 279180
 
4.7%
2 257963
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5910898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3557054
60.2%
1 881907
 
14.9%
5 650515
 
11.0%
4 284279
 
4.8%
7 279180
 
4.7%
2 257963
 
4.4%

BZONE DEMAND
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size69.9 MiB
5000
371335 
17500
284279 
9000
257963 
19000
226293 
4000
52887 

Length

Max length5
Median length4
Mean length4.4280604
Min length4

Characters and Unicode

Total characters5281600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4000
2nd row17500
3rd row19000
4th row9000
5th row9000

Common Values

ValueCountFrequency (%)
5000 371335
31.1%
17500 284279
23.8%
9000 257963
21.6%
19000 226293
19.0%
4000 52887
 
4.4%

Length

2023-07-09T00:10:51.203206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:51.440387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5000 371335
31.1%
17500 284279
23.8%
9000 257963
21.6%
19000 226293
19.0%
4000 52887
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 3293992
62.4%
5 655614
 
12.4%
1 510572
 
9.7%
9 484256
 
9.2%
7 284279
 
5.4%
4 52887
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5281600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3293992
62.4%
5 655614
 
12.4%
1 510572
 
9.7%
9 484256
 
9.2%
7 284279
 
5.4%
4 52887
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5281600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3293992
62.4%
5 655614
 
12.4%
1 510572
 
9.7%
9 484256
 
9.2%
7 284279
 
5.4%
4 52887
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5281600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3293992
62.4%
5 655614
 
12.4%
1 510572
 
9.7%
9 484256
 
9.2%
7 284279
 
5.4%
4 52887
 
1.0%

Z1
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing183824
Missing (%)15.4%
Memory size64.9 MiB
0.0
958021 
1180.0
 
36214
1920.0
 
14698

Length

Max length6
Median length3
Mean length3.1513837
Min length3

Characters and Unicode

Total characters3179535
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 958021
80.3%
1180.0 36214
 
3.0%
1920.0 14698
 
1.2%
(Missing) 183824
 
15.4%

Length

2023-07-09T00:10:51.656831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:51.882893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 958021
95.0%
1180.0 36214
 
3.6%
1920.0 14698
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 2017866
63.5%
. 1008933
31.7%
1 87126
 
2.7%
8 36214
 
1.1%
9 14698
 
0.5%
2 14698
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2170602
68.3%
Other Punctuation 1008933
31.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2017866
93.0%
1 87126
 
4.0%
8 36214
 
1.7%
9 14698
 
0.7%
2 14698
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 1008933
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3179535
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2017866
63.5%
. 1008933
31.7%
1 87126
 
2.7%
8 36214
 
1.1%
9 14698
 
0.5%
2 14698
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3179535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2017866
63.5%
. 1008933
31.7%
1 87126
 
2.7%
8 36214
 
1.1%
9 14698
 
0.5%
2 14698
 
0.5%

Z2
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing14698
Missing (%)1.2%
Memory size68.6 MiB
0.0
958021 
1920.0
220038 

Length

Max length6
Median length3
Mean length3.5603404
Min length3

Characters and Unicode

Total characters4194291
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 958021
80.3%
1920.0 220038
 
18.4%
(Missing) 14698
 
1.2%

Length

2023-07-09T00:10:52.071130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:52.286748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 958021
81.3%
1920.0 220038
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 2356118
56.2%
. 1178059
28.1%
1 220038
 
5.2%
9 220038
 
5.2%
2 220038
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3016232
71.9%
Other Punctuation 1178059
 
28.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2356118
78.1%
1 220038
 
7.3%
9 220038
 
7.3%
2 220038
 
7.3%
Other Punctuation
ValueCountFrequency (%)
. 1178059
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4194291
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2356118
56.2%
. 1178059
28.1%
1 220038
 
5.2%
9 220038
 
5.2%
2 220038
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4194291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2356118
56.2%
. 1178059
28.1%
1 220038
 
5.2%
9 220038
 
5.2%
2 220038
 
5.2%

Z3
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing12790
Missing (%)1.1%
Memory size68.3 MiB
0.0
1019944 
770.0
 
91707
140.0
 
36214
280.0
 
32102

Length

Max length5
Median length3
Mean length3.271233
Min length3

Characters and Unicode

Total characters3859947
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1019944
85.5%
770.0 91707
 
7.7%
140.0 36214
 
3.0%
280.0 32102
 
2.7%
(Missing) 12790
 
1.1%

Length

2023-07-09T00:10:52.473372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:52.720613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1019944
86.4%
770.0 91707
 
7.8%
140.0 36214
 
3.1%
280.0 32102
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 2359934
61.1%
. 1179967
30.6%
7 183414
 
4.8%
1 36214
 
0.9%
4 36214
 
0.9%
2 32102
 
0.8%
8 32102
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2679980
69.4%
Other Punctuation 1179967
30.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2359934
88.1%
7 183414
 
6.8%
1 36214
 
1.4%
4 36214
 
1.4%
2 32102
 
1.2%
8 32102
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 1179967
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3859947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2359934
61.1%
. 1179967
30.6%
7 183414
 
4.8%
1 36214
 
0.9%
4 36214
 
0.9%
2 32102
 
0.8%
8 32102
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3859947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2359934
61.1%
. 1179967
30.6%
7 183414
 
4.8%
1 36214
 
0.9%
4 36214
 
0.9%
2 32102
 
0.8%
8 32102
 
0.8%

Z4
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing36214
Missing (%)3.0%
Memory size68.2 MiB
0.0
913129 
1180.0
183824 
3100.0
 
32102
1920.0
 
14698
140.0
 
12790

Length

Max length6
Median length3
Mean length3.6203418
Min length3

Characters and Unicode

Total characters4187081
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 913129
76.6%
1180.0 183824
 
15.4%
3100.0 32102
 
2.7%
1920.0 14698
 
1.2%
140.0 12790
 
1.1%
(Missing) 36214
 
3.0%

Length

2023-07-09T00:10:52.918969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:53.154942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 913129
79.0%
1180.0 183824
 
15.9%
3100.0 32102
 
2.8%
1920.0 14698
 
1.3%
140.0 12790
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 2345188
56.0%
. 1156543
27.6%
1 427238
 
10.2%
8 183824
 
4.4%
3 32102
 
0.8%
9 14698
 
0.4%
2 14698
 
0.4%
4 12790
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3030538
72.4%
Other Punctuation 1156543
 
27.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2345188
77.4%
1 427238
 
14.1%
8 183824
 
6.1%
3 32102
 
1.1%
9 14698
 
0.5%
2 14698
 
0.5%
4 12790
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1156543
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4187081
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2345188
56.0%
. 1156543
27.6%
1 427238
 
10.2%
8 183824
 
4.4%
3 32102
 
0.8%
9 14698
 
0.4%
2 14698
 
0.4%
4 12790
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4187081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2345188
56.0%
. 1156543
27.6%
1 427238
 
10.2%
8 183824
 
4.4%
3 32102
 
0.8%
9 14698
 
0.4%
2 14698
 
0.4%
4 12790
 
0.3%

Z5
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing32102
Missing (%)2.7%
Memory size68.0 MiB
0.0
1019944 
3100.0
127921 
280.0
 
12790

Length

Max length6
Median length3
Mean length3.3526828
Min length3

Characters and Unicode

Total characters3891308
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1019944
85.5%
3100.0 127921
 
10.7%
280.0 12790
 
1.1%
(Missing) 32102
 
2.7%

Length

2023-07-09T00:10:53.369970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:53.596944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1019944
87.9%
3100.0 127921
 
11.0%
280.0 12790
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 2449231
62.9%
. 1160655
29.8%
3 127921
 
3.3%
1 127921
 
3.3%
2 12790
 
0.3%
8 12790
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2730653
70.2%
Other Punctuation 1160655
29.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2449231
89.7%
3 127921
 
4.7%
1 127921
 
4.7%
2 12790
 
0.5%
8 12790
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 1160655
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3891308
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2449231
62.9%
. 1160655
29.8%
3 127921
 
3.3%
1 127921
 
3.3%
2 12790
 
0.3%
8 12790
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3891308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2449231
62.9%
. 1160655
29.8%
3 127921
 
3.3%
1 127921
 
3.3%
2 12790
 
0.3%
8 12790
 
0.3%

Z6
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing91707
Missing (%)7.7%
Memory size67.2 MiB
0.0
854081 
2250.0
188279 
3100.0
 
32102
2800.0
 
13798
770.0
 
12790

Length

Max length6
Median length3
Mean length3.6612933
Min length3

Characters and Unicode

Total characters4031267
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2250.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 854081
71.6%
2250.0 188279
 
15.8%
3100.0 32102
 
2.7%
2800.0 13798
 
1.2%
770.0 12790
 
1.1%
(Missing) 91707
 
7.7%

Length

2023-07-09T00:10:53.801003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:54.035700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 854081
77.6%
2250.0 188279
 
17.1%
3100.0 32102
 
2.9%
2800.0 13798
 
1.3%
770.0 12790
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 2248000
55.8%
. 1101050
27.3%
2 390356
 
9.7%
5 188279
 
4.7%
3 32102
 
0.8%
1 32102
 
0.8%
7 25580
 
0.6%
8 13798
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2930217
72.7%
Other Punctuation 1101050
 
27.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2248000
76.7%
2 390356
 
13.3%
5 188279
 
6.4%
3 32102
 
1.1%
1 32102
 
1.1%
7 25580
 
0.9%
8 13798
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 1101050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4031267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2248000
55.8%
. 1101050
27.3%
2 390356
 
9.7%
5 188279
 
4.7%
3 32102
 
0.8%
1 32102
 
0.8%
7 25580
 
0.6%
8 13798
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4031267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2248000
55.8%
. 1101050
27.3%
2 390356
 
9.7%
5 188279
 
4.7%
3 32102
 
0.8%
1 32102
 
0.8%
7 25580
 
0.6%
8 13798
 
0.3%

Z7
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing13798
Missing (%)1.2%
Memory size69.2 MiB
0.0
760603 
2800.0
230077 
1400.0
188279 

Length

Max length6
Median length3
Mean length4.0645561
Min length3

Characters and Unicode

Total characters4791945
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1400.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 760603
63.8%
2800.0 230077
 
19.3%
1400.0 188279
 
15.8%
(Missing) 13798
 
1.2%

Length

2023-07-09T00:10:54.254563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:54.498444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 760603
64.5%
2800.0 230077
 
19.5%
1400.0 188279
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 2776274
57.9%
. 1178959
24.6%
2 230077
 
4.8%
8 230077
 
4.8%
1 188279
 
3.9%
4 188279
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3612986
75.4%
Other Punctuation 1178959
 
24.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2776274
76.8%
2 230077
 
6.4%
8 230077
 
6.4%
1 188279
 
5.2%
4 188279
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 1178959
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4791945
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2776274
57.9%
. 1178959
24.6%
2 230077
 
4.8%
8 230077
 
4.8%
1 188279
 
3.9%
4 188279
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4791945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2776274
57.9%
. 1178959
24.6%
2 230077
 
4.8%
8 230077
 
4.8%
1 188279
 
3.9%
4 188279
 
3.9%

Z8
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing138370
Missing (%)11.6%
Memory size66.5 MiB
0.0
746515 
3980.0
188279 
4240.0
105795 
2800.0
 
13798

Length

Max length6
Median length3
Mean length3.8759744
Min length3

Characters and Unicode

Total characters4086777
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row3980.0
4th row4240.0
5th row4240.0

Common Values

ValueCountFrequency (%)
0.0 746515
62.6%
3980.0 188279
 
15.8%
4240.0 105795
 
8.9%
2800.0 13798
 
1.2%
(Missing) 138370
 
11.6%

Length

2023-07-09T00:10:54.719276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:54.966666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 746515
70.8%
3980.0 188279
 
17.9%
4240.0 105795
 
10.0%
2800.0 13798
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2122572
51.9%
. 1054387
25.8%
4 211590
 
5.2%
8 202077
 
4.9%
3 188279
 
4.6%
9 188279
 
4.6%
2 119593
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3032390
74.2%
Other Punctuation 1054387
 
25.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2122572
70.0%
4 211590
 
7.0%
8 202077
 
6.7%
3 188279
 
6.2%
9 188279
 
6.2%
2 119593
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 1054387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4086777
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2122572
51.9%
. 1054387
25.8%
4 211590
 
5.2%
8 202077
 
4.9%
3 188279
 
4.6%
9 188279
 
4.6%
2 119593
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4086777
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2122572
51.9%
. 1054387
25.8%
4 211590
 
5.2%
8 202077
 
4.9%
3 188279
 
4.6%
9 188279
 
4.6%
2 119593
 
2.9%

Z9
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing188279
Missing (%)15.8%
Memory size65.5 MiB
0.0
722589 
3980.0
138370 
2250.0
91707 
2800.0
 
38014
1400.0
 
13798

Length

Max length6
Median length3
Mean length3.841897
Min length3

Characters and Unicode

Total characters3859101
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 722589
60.6%
3980.0 138370
 
11.6%
2250.0 91707
 
7.7%
2800.0 38014
 
3.2%
1400.0 13798
 
1.2%
(Missing) 188279
 
15.8%

Length

2023-07-09T00:10:55.180472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:55.428781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 722589
71.9%
3980.0 138370
 
13.8%
2250.0 91707
 
9.1%
2800.0 38014
 
3.8%
1400.0 13798
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2060768
53.4%
. 1004478
26.0%
2 221428
 
5.7%
8 176384
 
4.6%
3 138370
 
3.6%
9 138370
 
3.6%
5 91707
 
2.4%
1 13798
 
0.4%
4 13798
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2854623
74.0%
Other Punctuation 1004478
 
26.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2060768
72.2%
2 221428
 
7.8%
8 176384
 
6.2%
3 138370
 
4.8%
9 138370
 
4.8%
5 91707
 
3.2%
1 13798
 
0.5%
4 13798
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 1004478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3859101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2060768
53.4%
. 1004478
26.0%
2 221428
 
5.7%
8 176384
 
4.6%
3 138370
 
3.6%
9 138370
 
3.6%
5 91707
 
2.4%
1 13798
 
0.4%
4 13798
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3859101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2060768
53.4%
. 1004478
26.0%
2 221428
 
5.7%
8 176384
 
4.6%
3 138370
 
3.6%
9 138370
 
3.6%
5 91707
 
2.4%
1 13798
 
0.4%
4 13798
 
0.4%

Z10
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing105795
Missing (%)8.9%
Memory size66.9 MiB
0.0
857691 
4240.0
138370 
4200.0
 
52887
1400.0
 
38014

Length

Max length6
Median length3
Mean length3.6327848
Min length3

Characters and Unicode

Total characters3948699
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4200.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 857691
71.9%
4240.0 138370
 
11.6%
4200.0 52887
 
4.4%
1400.0 38014
 
3.2%
(Missing) 105795
 
8.9%

Length

2023-07-09T00:10:55.659093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:55.883325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 857691
78.9%
4240.0 138370
 
12.7%
4200.0 52887
 
4.9%
1400.0 38014
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 2264825
57.4%
. 1086962
27.5%
4 367641
 
9.3%
2 191257
 
4.8%
1 38014
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2861737
72.5%
Other Punctuation 1086962
 
27.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2264825
79.1%
4 367641
 
12.8%
2 191257
 
6.7%
1 38014
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 1086962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3948699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2264825
57.4%
. 1086962
27.5%
4 367641
 
9.3%
2 191257
 
4.8%
1 38014
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3948699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2264825
57.4%
. 1086962
27.5%
4 367641
 
9.3%
2 191257
 
4.8%
1 38014
 
1.0%

Z11
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing38014
Missing (%)3.2%
Memory size68.9 MiB
0.0
690617 
1400.0
275847 
2800.0
188279 

Length

Max length6
Median length3
Mean length4.2057904
Min length3

Characters and Unicode

Total characters4856607
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1400.0
2nd row0.0
3rd row2800.0
4th row1400.0
5th row1400.0

Common Values

ValueCountFrequency (%)
0.0 690617
57.9%
1400.0 275847
 
23.1%
2800.0 188279
 
15.8%
(Missing) 38014
 
3.2%

Length

2023-07-09T00:10:56.087427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:56.323712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 690617
59.8%
1400.0 275847
 
23.9%
2800.0 188279
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 2773612
57.1%
. 1154743
23.8%
1 275847
 
5.7%
4 275847
 
5.7%
2 188279
 
3.9%
8 188279
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3701864
76.2%
Other Punctuation 1154743
 
23.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2773612
74.9%
1 275847
 
7.5%
4 275847
 
7.5%
2 188279
 
5.1%
8 188279
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 1154743
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4856607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2773612
57.1%
. 1154743
23.8%
1 275847
 
5.7%
4 275847
 
5.7%
2 188279
 
3.9%
8 188279
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4856607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2773612
57.1%
. 1154743
23.8%
1 275847
 
5.7%
4 275847
 
5.7%
2 188279
 
3.9%
8 188279
 
3.9%

Z12
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing52887
Missing (%)4.4%
Memory size68.2 MiB
0.0
790442 
1400.0
224788 
4200.0
124640 

Length

Max length6
Median length3
Mean length3.9196522
Min length3

Characters and Unicode

Total characters4467894
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row4200.0
4th row4200.0
5th row1400.0

Common Values

ValueCountFrequency (%)
0.0 790442
66.3%
1400.0 224788
 
18.8%
4200.0 124640
 
10.4%
(Missing) 52887
 
4.4%

Length

2023-07-09T00:10:56.527812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:56.755673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 790442
69.3%
1400.0 224788
 
19.7%
4200.0 124640
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 2629168
58.8%
. 1139870
25.5%
4 349428
 
7.8%
1 224788
 
5.0%
2 124640
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3328024
74.5%
Other Punctuation 1139870
 
25.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2629168
79.0%
4 349428
 
10.5%
1 224788
 
6.8%
2 124640
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 1139870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4467894
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2629168
58.8%
. 1139870
25.5%
4 349428
 
7.8%
1 224788
 
5.0%
2 124640
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4467894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2629168
58.8%
. 1139870
25.5%
4 349428
 
7.8%
1 224788
 
5.0%
2 124640
 
2.8%

Z13
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing117165
Missing (%)9.8%
Memory size66.5 MiB
0.0
906031 
1400.0
122255 
2800.0
 
47306

Length

Max length6
Median length3
Mean length3.472933
Min length3

Characters and Unicode

Total characters3735459
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1400.0
2nd row2800.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 906031
76.0%
1400.0 122255
 
10.2%
2800.0 47306
 
4.0%
(Missing) 117165
 
9.8%

Length

2023-07-09T00:10:56.953559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:57.185344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 906031
84.2%
1400.0 122255
 
11.4%
2800.0 47306
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 2320745
62.1%
. 1075592
28.8%
1 122255
 
3.3%
4 122255
 
3.3%
2 47306
 
1.3%
8 47306
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2659867
71.2%
Other Punctuation 1075592
28.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2320745
87.3%
1 122255
 
4.6%
4 122255
 
4.6%
2 47306
 
1.8%
8 47306
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 1075592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3735459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2320745
62.1%
. 1075592
28.8%
1 122255
 
3.3%
4 122255
 
3.3%
2 47306
 
1.3%
8 47306
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3735459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2320745
62.1%
. 1075592
28.8%
1 122255
 
3.3%
4 122255
 
3.3%
2 47306
 
1.3%
8 47306
 
1.3%

Z14
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing18845
Missing (%)1.6%
Memory size68.4 MiB
0.0
1004110 
6560.0
 
69609
4200.0
 
52887
1400.0
 
47306

Length

Max length6
Median length3
Mean length3.4339388
Min length3

Characters and Unicode

Total characters4031142
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4200.0
2nd row1400.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1004110
84.2%
6560.0 69609
 
5.8%
4200.0 52887
 
4.4%
1400.0 47306
 
4.0%
(Missing) 18845
 
1.6%

Length

2023-07-09T00:10:57.397243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:57.638709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1004110
85.5%
6560.0 69609
 
5.9%
4200.0 52887
 
4.5%
1400.0 47306
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 2448017
60.7%
. 1173912
29.1%
6 139218
 
3.5%
4 100193
 
2.5%
5 69609
 
1.7%
2 52887
 
1.3%
1 47306
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2857230
70.9%
Other Punctuation 1173912
29.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2448017
85.7%
6 139218
 
4.9%
4 100193
 
3.5%
5 69609
 
2.4%
2 52887
 
1.9%
1 47306
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 1173912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4031142
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2448017
60.7%
. 1173912
29.1%
6 139218
 
3.5%
4 100193
 
2.5%
5 69609
 
1.7%
2 52887
 
1.3%
1 47306
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4031142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2448017
60.7%
. 1173912
29.1%
6 139218
 
3.5%
4 100193
 
2.5%
5 69609
 
1.7%
2 52887
 
1.3%
1 47306
 
1.2%

Z15
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing69609
Missing (%)5.8%
Memory size67.3 MiB
0.0
1004110 
1400.0
 
100193
6560.0
 
18845

Length

Max length6
Median length3
Mean length3.3179581
Min length3

Characters and Unicode

Total characters3726558
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1400.0
2nd row1400.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1004110
84.2%
1400.0 100193
 
8.4%
6560.0 18845
 
1.6%
(Missing) 69609
 
5.8%

Length

2023-07-09T00:10:57.847244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:58.078962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1004110
89.4%
1400.0 100193
 
8.9%
6560.0 18845
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 2346489
63.0%
. 1123148
30.1%
1 100193
 
2.7%
4 100193
 
2.7%
6 37690
 
1.0%
5 18845
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2603410
69.9%
Other Punctuation 1123148
30.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2346489
90.1%
1 100193
 
3.8%
4 100193
 
3.8%
6 37690
 
1.4%
5 18845
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 1123148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3726558
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2346489
63.0%
. 1123148
30.1%
1 100193
 
2.7%
4 100193
 
2.7%
6 37690
 
1.0%
5 18845
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3726558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2346489
63.0%
. 1123148
30.1%
1 100193
 
2.7%
4 100193
 
2.7%
6 37690
 
1.0%
5 18845
 
0.5%

Z16
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing47306
Missing (%)4.0%
Memory size68.0 MiB
0.0
908478 
1400.0
119808 
2800.0
117165 

Length

Max length6
Median length3
Mean length3.6206455
Min length3

Characters and Unicode

Total characters4147272
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1400.0

Common Values

ValueCountFrequency (%)
0.0 908478
76.2%
1400.0 119808
 
10.0%
2800.0 117165
 
9.8%
(Missing) 47306
 
4.0%

Length

2023-07-09T00:10:58.267811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:58.489536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 908478
79.3%
1400.0 119808
 
10.5%
2800.0 117165
 
10.2%

Most occurring characters

ValueCountFrequency (%)
0 2527875
61.0%
. 1145451
27.6%
1 119808
 
2.9%
4 119808
 
2.9%
2 117165
 
2.8%
8 117165
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3001821
72.4%
Other Punctuation 1145451
 
27.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2527875
84.2%
1 119808
 
4.0%
4 119808
 
4.0%
2 117165
 
3.9%
8 117165
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 1145451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4147272
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2527875
61.0%
. 1145451
27.6%
1 119808
 
2.9%
4 119808
 
2.9%
2 117165
 
2.8%
8 117165
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4147272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2527875
61.0%
. 1145451
27.6%
1 119808
 
2.9%
4 119808
 
2.9%
2 117165
 
2.8%
8 117165
 
2.8%

Z17
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing31354
Missing (%)2.6%
Memory size68.1 MiB
0.0
996932 
1400.0
164471 

Length

Max length6
Median length3
Mean length3.4248422
Min length3

Characters and Unicode

Total characters3977622
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1400.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 996932
83.6%
1400.0 164471
 
13.8%
(Missing) 31354
 
2.6%

Length

2023-07-09T00:10:58.696969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:10:58.912624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 996932
85.8%
1400.0 164471
 
14.2%

Most occurring characters

ValueCountFrequency (%)
0 2487277
62.5%
. 1161403
29.2%
1 164471
 
4.1%
4 164471
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2816219
70.8%
Other Punctuation 1161403
29.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2487277
88.3%
1 164471
 
5.8%
4 164471
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 1161403
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3977622
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2487277
62.5%
. 1161403
29.2%
1 164471
 
4.1%
4 164471
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3977622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2487277
62.5%
. 1161403
29.2%
1 164471
 
4.1%
4 164471
 
4.1%

BMU Party ID
Categorical

Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.9 MiB
EECL
132455 
SSEGEN
108928 
INNOGY01
108444 
STATKRA1
 
59375
WESTBURB
 
56438
Other values (128)
727117 

Length

Max length8
Median length7
Mean length6.2176001
Min length2

Characters and Unicode

Total characters7416086
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINNOGY01
2nd rowSPGEN01
3rd rowCARRINGT
4th rowEECL
5th rowEECL

Common Values

ValueCountFrequency (%)
EECL 132455
 
11.1%
SSEGEN 108928
 
9.1%
INNOGY01 108444
 
9.1%
STATKRA1 59375
 
5.0%
WESTBURB 56438
 
4.7%
FSTHYDRO 43573
 
3.7%
CONRAD 39795
 
3.3%
EPUKI 37908
 
3.2%
CARRINGT 36174
 
3.0%
FLEXTRCY 30567
 
2.6%
Other values (123) 539100
45.2%

Length

2023-07-09T00:10:59.107484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eecl 132455
 
11.1%
ssegen 108928
 
9.1%
innogy01 108444
 
9.1%
statkra1 59375
 
5.0%
westburb 56438
 
4.7%
fsthydro 43573
 
3.7%
conrad 39795
 
3.3%
epuki 37908
 
3.2%
carringt 36174
 
3.0%
flextrcy 30567
 
2.6%
Other values (123) 539100
45.2%

Most occurring characters

ValueCountFrequency (%)
E 888092
 
12.0%
S 655152
 
8.8%
N 595921
 
8.0%
R 485229
 
6.5%
L 464319
 
6.3%
A 422688
 
5.7%
T 396220
 
5.3%
C 369009
 
5.0%
G 362003
 
4.9%
O 297757
 
4.0%
Other values (24) 2479696
33.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6878338
92.7%
Decimal Number 537748
 
7.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 888092
12.9%
S 655152
 
9.5%
N 595921
 
8.7%
R 485229
 
7.1%
L 464319
 
6.8%
A 422688
 
6.1%
T 396220
 
5.8%
C 369009
 
5.4%
G 362003
 
5.3%
O 297757
 
4.3%
Other values (15) 1941948
28.2%
Decimal Number
ValueCountFrequency (%)
1 260408
48.4%
0 241238
44.9%
2 20694
 
3.8%
6 6642
 
1.2%
3 5486
 
1.0%
7 2081
 
0.4%
9 594
 
0.1%
5 378
 
0.1%
8 227
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6878338
92.7%
Common 537748
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 888092
12.9%
S 655152
 
9.5%
N 595921
 
8.7%
R 485229
 
7.1%
L 464319
 
6.8%
A 422688
 
6.1%
T 396220
 
5.8%
C 369009
 
5.4%
G 362003
 
5.3%
O 297757
 
4.3%
Other values (15) 1941948
28.2%
Common
ValueCountFrequency (%)
1 260408
48.4%
0 241238
44.9%
2 20694
 
3.8%
6 6642
 
1.2%
3 5486
 
1.0%
7 2081
 
0.4%
9 594
 
0.1%
5 378
 
0.1%
8 227
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7416086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 888092
 
12.0%
S 655152
 
8.8%
N 595921
 
8.0%
R 485229
 
6.5%
L 464319
 
6.3%
A 422688
 
5.7%
T 396220
 
5.3%
C 369009
 
5.0%
G 362003
 
4.9%
O 297757
 
4.0%
Other values (24) 2479696
33.4%

BMU Party Name
Categorical

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size88.5 MiB
Uniper UK Limited
132455 
SSE Generation Ltd
108928 
RWE Generation UK plc
108444 
Statkraft Markets Gmbh
 
59375
West Burton B Limited
 
56438
Other values (129)
727117 

Length

Max length31
Median length28
Mean length20.830084
Min length4

Characters and Unicode

Total characters24845229
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRWE Generation UK plc
2nd rowVPI Power Limited
3rd rowCarrington Power Ltd
4th rowUniper UK Limited
5th rowUniper UK Limited

Common Values

ValueCountFrequency (%)
Uniper UK Limited 132455
 
11.1%
SSE Generation Ltd 108928
 
9.1%
RWE Generation UK plc 108444
 
9.1%
Statkraft Markets Gmbh 59375
 
5.0%
West Burton B Limited 56438
 
4.7%
First Hydro Company 43573
 
3.7%
Conrad Energy (Trading) 39795
 
3.3%
EP UK INVESTMENTS LIMITED 37908
 
3.2%
Carrington Power Ltd 36174
 
3.0%
Flexitricity Limited 30567
 
2.6%
Other values (124) 539100
45.2%

Length

2023-07-09T00:10:59.344696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
limited 558355
 
14.2%
uk 329438
 
8.4%
ltd 294253
 
7.5%
generation 240969
 
6.1%
power 197998
 
5.0%
energy 137603
 
3.5%
uniper 132455
 
3.4%
sse 124719
 
3.2%
rwe 108698
 
2.8%
plc 108506
 
2.8%
Other values (192) 1691818
43.1%

Most occurring characters

ValueCountFrequency (%)
2732087
 
11.0%
e 2232184
 
9.0%
i 1876986
 
7.6%
t 1720273
 
6.9%
r 1602756
 
6.5%
n 1387165
 
5.6%
d 1167964
 
4.7%
a 1166121
 
4.7%
o 957595
 
3.9%
L 935988
 
3.8%
Other values (46) 9066110
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16295602
65.6%
Uppercase Letter 5637787
 
22.7%
Space Separator 2732087
 
11.0%
Close Punctuation 80566
 
0.3%
Open Punctuation 80566
 
0.3%
Other Punctuation 13499
 
0.1%
Decimal Number 5122
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2232184
13.7%
i 1876986
11.5%
t 1720273
10.6%
r 1602756
9.8%
n 1387165
8.5%
d 1167964
 
7.2%
a 1166121
 
7.2%
o 957595
 
5.9%
m 875963
 
5.4%
p 386861
 
2.4%
Other values (14) 2921734
17.9%
Uppercase Letter
ValueCountFrequency (%)
L 935988
16.6%
E 581016
10.3%
S 563864
10.0%
U 464710
 
8.2%
K 343880
 
6.1%
P 343386
 
6.1%
G 329883
 
5.9%
W 310118
 
5.5%
C 276372
 
4.9%
M 205354
 
3.6%
Other values (14) 1283216
22.8%
Decimal Number
ValueCountFrequency (%)
2 3539
69.1%
1 1227
 
24.0%
3 356
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 10081
74.7%
& 3418
 
25.3%
Space Separator
ValueCountFrequency (%)
2732087
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80566
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21933389
88.3%
Common 2911840
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2232184
 
10.2%
i 1876986
 
8.6%
t 1720273
 
7.8%
r 1602756
 
7.3%
n 1387165
 
6.3%
d 1167964
 
5.3%
a 1166121
 
5.3%
o 957595
 
4.4%
L 935988
 
4.3%
m 875963
 
4.0%
Other values (38) 8010394
36.5%
Common
ValueCountFrequency (%)
2732087
93.8%
) 80566
 
2.8%
( 80566
 
2.8%
. 10081
 
0.3%
2 3539
 
0.1%
& 3418
 
0.1%
1 1227
 
< 0.1%
3 356
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24845229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2732087
 
11.0%
e 2232184
 
9.0%
i 1876986
 
7.6%
t 1720273
 
6.9%
r 1602756
 
6.5%
n 1387165
 
5.6%
d 1167964
 
4.7%
a 1166121
 
4.7%
o 957595
 
3.9%
L 935988
 
3.8%
Other values (46) 9066110
36.5%

Trading Unit
Categorical

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.2 MiB
Non-TrUnit
474734 
West Burton A&B Power Stations
57364 
DEFAULT__P
53345 
GRAIN PS TRADING UNIT
 
46721
Pembroke Power Station
 
41024
Other values (34)
519569 

Length

Max length30
Median length10
Mean length14.376746
Min length8

Characters and Unicode

Total characters17147964
Distinct characters53
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-TrUnit
2nd rowNon-TrUnit
3rd rowNon-TrUnit
4th rowNon-TrUnit
5th rowNon-TrUnit

Common Values

ValueCountFrequency (%)
Non-TrUnit 474734
39.8%
West Burton A&B Power Stations 57364
 
4.8%
DEFAULT__P 53345
 
4.5%
GRAIN PS TRADING UNIT 46721
 
3.9%
Pembroke Power Station 41024
 
3.4%
DEFAULT__G 37095
 
3.1%
DINORWIG 33650
 
2.8%
Staythorpe Power Station 31275
 
2.6%
CONNAHS QUAY PS TRADING UNIT 31253
 
2.6%
SALTEND1 29543
 
2.5%
Other values (29) 356753
29.9%

Length

2023-07-09T00:10:59.591701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
non-trunit 474734
20.8%
power 245708
 
10.7%
station 188344
 
8.2%
trading 120879
 
5.3%
unit 120879
 
5.3%
ps 100909
 
4.4%
west 57364
 
2.5%
burton 57364
 
2.5%
a&b 57364
 
2.5%
stations 57364
 
2.5%
Other values (47) 805112
35.2%

Most occurring characters

ValueCountFrequency (%)
n 1379131
 
8.0%
t 1204005
 
7.0%
o 1111862
 
6.5%
1093264
 
6.4%
T 1020052
 
5.9%
U 928265
 
5.4%
r 924236
 
5.4%
N 885081
 
5.2%
i 844728
 
4.9%
A 607207
 
3.5%
Other values (43) 7150133
41.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7502612
43.8%
Lowercase Letter 7437625
43.4%
Space Separator 1093264
 
6.4%
Connector Punctuation 502846
 
2.9%
Dash Punctuation 474734
 
2.8%
Other Punctuation 107340
 
0.6%
Decimal Number 29543
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1379131
18.5%
t 1204005
16.2%
o 1111862
14.9%
r 924236
12.4%
i 844728
11.4%
e 464086
 
6.2%
a 457105
 
6.1%
w 226337
 
3.0%
s 136094
 
1.8%
u 78377
 
1.1%
Other values (13) 611664
8.2%
Uppercase Letter
ValueCountFrequency (%)
T 1020052
13.6%
U 928265
12.4%
N 885081
11.8%
A 607207
 
8.1%
S 532301
 
7.1%
P 478381
 
6.4%
D 446438
 
6.0%
E 361913
 
4.8%
I 317037
 
4.2%
L 308666
 
4.1%
Other values (13) 1617271
21.6%
Other Punctuation
ValueCountFrequency (%)
& 57364
53.4%
. 24988
23.3%
/ 24988
23.3%
Space Separator
ValueCountFrequency (%)
1093264
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 502846
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 474734
100.0%
Decimal Number
ValueCountFrequency (%)
1 29543
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14940237
87.1%
Common 2207727
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1379131
 
9.2%
t 1204005
 
8.1%
o 1111862
 
7.4%
T 1020052
 
6.8%
U 928265
 
6.2%
r 924236
 
6.2%
N 885081
 
5.9%
i 844728
 
5.7%
A 607207
 
4.1%
S 532301
 
3.6%
Other values (36) 5503369
36.8%
Common
ValueCountFrequency (%)
1093264
49.5%
_ 502846
22.8%
- 474734
21.5%
& 57364
 
2.6%
1 29543
 
1.3%
. 24988
 
1.1%
/ 24988
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17147964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1379131
 
8.0%
t 1204005
 
7.0%
o 1111862
 
6.5%
1093264
 
6.4%
T 1020052
 
5.9%
U 928265
 
5.4%
r 924236
 
5.4%
N 885081
 
5.2%
i 844728
 
4.9%
A 607207
 
3.5%
Other values (43) 7150133
41.7%

PC Flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.0 MiB
Dynamic
1033753 
Consumption (C)
 
95740
Production (P)
 
63264

Length

Max length15
Median length7
Mean length8.0134235
Min length7

Characters and Unicode

Total characters9558067
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDynamic
2nd rowDynamic
3rd rowDynamic
4th rowDynamic
5th rowDynamic

Common Values

ValueCountFrequency (%)
Dynamic 1033753
86.7%
Consumption (C) 95740
 
8.0%
Production (P) 63264
 
5.3%

Length

2023-07-09T00:10:59.826902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:11:00.063845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dynamic 1033753
76.5%
consumption 95740
 
7.1%
c 95740
 
7.1%
production 63264
 
4.7%
p 63264
 
4.7%

Most occurring characters

ValueCountFrequency (%)
n 1288497
13.5%
i 1192757
12.5%
m 1129493
11.8%
c 1097017
11.5%
D 1033753
10.8%
a 1033753
10.8%
y 1033753
10.8%
o 318008
 
3.3%
C 191480
 
2.0%
) 159004
 
1.7%
Other values (9) 1080552
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7729294
80.9%
Uppercase Letter 1351761
 
14.1%
Close Punctuation 159004
 
1.7%
Space Separator 159004
 
1.7%
Open Punctuation 159004
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1288497
16.7%
i 1192757
15.4%
m 1129493
14.6%
c 1097017
14.2%
a 1033753
13.4%
y 1033753
13.4%
o 318008
 
4.1%
u 159004
 
2.1%
t 159004
 
2.1%
s 95740
 
1.2%
Other values (3) 222268
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
D 1033753
76.5%
C 191480
 
14.2%
P 126528
 
9.4%
Close Punctuation
ValueCountFrequency (%)
) 159004
100.0%
Space Separator
ValueCountFrequency (%)
159004
100.0%
Open Punctuation
ValueCountFrequency (%)
( 159004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9081055
95.0%
Common 477012
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1288497
14.2%
i 1192757
13.1%
m 1129493
12.4%
c 1097017
12.1%
D 1033753
11.4%
a 1033753
11.4%
y 1033753
11.4%
o 318008
 
3.5%
C 191480
 
2.1%
u 159004
 
1.8%
Other values (6) 603540
6.6%
Common
ValueCountFrequency (%)
) 159004
33.3%
159004
33.3%
( 159004
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9558067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1288497
13.5%
i 1192757
12.5%
m 1129493
11.8%
c 1097017
11.5%
D 1033753
10.8%
a 1033753
10.8%
y 1033753
10.8%
o 318008
 
3.3%
C 191480
 
2.0%
) 159004
 
1.7%
Other values (9) 1080552
11.3%

PC Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.0 MiB
Production (P)
939066 
Consumption (C)
253691 

Length

Max length15
Median length14
Mean length14.212693
Min length14

Characters and Unicode

Total characters16952289
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProduction (P)
2nd rowProduction (P)
3rd rowProduction (P)
4th rowProduction (P)
5th rowProduction (P)

Common Values

ValueCountFrequency (%)
Production (P) 939066
78.7%
Consumption (C) 253691
 
21.3%

Length

2023-07-09T00:11:00.249814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:11:00.459322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
production 939066
39.4%
p 939066
39.4%
consumption 253691
 
10.6%
c 253691
 
10.6%

Most occurring characters

ValueCountFrequency (%)
o 2385514
14.1%
P 1878132
11.1%
n 1446448
8.5%
u 1192757
 
7.0%
t 1192757
 
7.0%
i 1192757
 
7.0%
1192757
 
7.0%
( 1192757
 
7.0%
) 1192757
 
7.0%
r 939066
 
5.5%
Other values (6) 3146587
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10988504
64.8%
Uppercase Letter 2385514
 
14.1%
Space Separator 1192757
 
7.0%
Open Punctuation 1192757
 
7.0%
Close Punctuation 1192757
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2385514
21.7%
n 1446448
13.2%
u 1192757
10.9%
t 1192757
10.9%
i 1192757
10.9%
r 939066
 
8.5%
d 939066
 
8.5%
c 939066
 
8.5%
s 253691
 
2.3%
m 253691
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
P 1878132
78.7%
C 507382
 
21.3%
Space Separator
ValueCountFrequency (%)
1192757
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1192757
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1192757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13374018
78.9%
Common 3578271
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2385514
17.8%
P 1878132
14.0%
n 1446448
10.8%
u 1192757
8.9%
t 1192757
8.9%
i 1192757
8.9%
r 939066
 
7.0%
d 939066
 
7.0%
c 939066
 
7.0%
C 507382
 
3.8%
Other values (3) 761073
 
5.7%
Common
ValueCountFrequency (%)
1192757
33.3%
( 1192757
33.3%
) 1192757
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16952289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2385514
14.1%
P 1878132
11.1%
n 1446448
8.5%
u 1192757
 
7.0%
t 1192757
 
7.0%
i 1192757
 
7.0%
1192757
 
7.0%
( 1192757
 
7.0%
) 1192757
 
7.0%
r 939066
 
5.5%
Other values (6) 3146587
18.6%

Transmission Loss Factor
Real number (ℝ)

Distinct164
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0045260688
Minimum-0.0522058
Maximum0.0315465
Zeros614
Zeros (%)0.1%
Negative557748
Negative (%)46.8%
Memory size9.1 MiB
2023-07-09T00:11:00.672425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0522058
5-th percentile-0.0522058
Q1-0.0188698
median0.0037283
Q30.0118813
95-th percentile0.0298018
Maximum0.0315465
Range0.0837523
Interquartile range (IQR)0.0307511

Descriptive statistics

Standard deviation0.024427863
Coefficient of variation (CV)-5.3971479
Kurtosis-0.764343
Mean-0.0045260688
Median Absolute Deviation (MAD)0.0136704
Skewness-0.57481713
Sum-5398.5003
Variance0.00059672049
MonotonicityNot monotonic
2023-07-09T00:11:00.913495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0398132 128682
 
10.8%
-0.0522058 90048
 
7.5%
0.0118813 65854
 
5.5%
0.0044415 56372
 
4.7%
0.0037283 55650
 
4.7%
0.0100104 55546
 
4.7%
-0.0060379 50385
 
4.2%
-0.0381051 46451
 
3.9%
-0.0090952 43926
 
3.7%
-0.010788 43417
 
3.6%
Other values (154) 556426
46.7%
ValueCountFrequency (%)
-0.0522058 90048
7.5%
-0.0399698 49
 
< 0.1%
-0.0398132 128682
10.8%
-0.0381051 46451
 
3.9%
-0.0374553 409
 
< 0.1%
-0.037195 69
 
< 0.1%
-0.0351173 2322
 
0.2%
-0.029104 1042
 
0.1%
-0.0261238 2591
 
0.2%
-0.0242162 328
 
< 0.1%
ValueCountFrequency (%)
0.0315465 24734
2.1%
0.0314595 29226
2.5%
0.0309432 2356
 
0.2%
0.0298018 20210
1.7%
0.0250176 21510
1.8%
0.0245493 825
 
0.1%
0.0241869 26279
2.2%
0.0238107 5982
 
0.5%
0.0237533 228
 
< 0.1%
0.0229594 36681
3.1%

Generation Capacity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct321
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.57239
Minimum0
Maximum1283
Zeros26692
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size9.1 MiB
2023-07-09T00:11:01.166723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q149
median320
Q3470
95-th percentile920
Maximum1283
Range1283
Interquartile range (IQR)421

Descriptive statistics

Standard deviation305.86074
Coefficient of variation (CV)0.92524589
Kurtosis0.086976086
Mean330.57239
Median Absolute Deviation (MAD)250
Skewness0.86366007
Sum3.9429253 × 108
Variance93550.794
MonotonicityNot monotonic
2023-07-09T00:11:01.456920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470 92491
 
7.8%
20 51551
 
4.3%
468 46527
 
3.9%
475 41024
 
3.4%
50 34713
 
2.9%
320 33650
 
2.8%
464 31275
 
2.6%
49 27278
 
2.3%
0 26692
 
2.2%
920 25738
 
2.2%
Other values (311) 781818
65.5%
ValueCountFrequency (%)
0 26692
2.2%
1.9 4
 
< 0.1%
2 11
 
< 0.1%
4 587
 
< 0.1%
4.7 8
 
< 0.1%
5 945
 
0.1%
5.88 1909
 
0.2%
6 5094
 
0.4%
6.121 145
 
< 0.1%
7.12 1011
 
0.1%
ValueCountFrequency (%)
1283 11407
1.0%
1206.288 26
 
< 0.1%
1206.28 28
 
< 0.1%
1200 13235
1.1%
950 13143
1.1%
920 25738
2.2%
905 12920
1.1%
887.26 1632
 
0.1%
879.76 1118
 
0.1%
870.616 329
 
< 0.1%

Demand Capacity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-23.291824
Minimum-366.184
Maximum0
Zeros231544
Zeros (%)19.4%
Negative961213
Negative (%)80.6%
Memory size9.1 MiB
2023-07-09T00:11:01.723340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-366.184
5-th percentile-134
Q1-15
median-10
Q3-1
95-th percentile0
Maximum0
Range366.184
Interquartile range (IQR)14

Descriptive statistics

Standard deviation54.496831
Coefficient of variation (CV)-2.3397408
Kurtosis16.071144
Mean-23.291824
Median Absolute Deviation (MAD)9
Skewness-3.9714321
Sum-27781486
Variance2969.9045
MonotonicityNot monotonic
2023-07-09T00:11:01.988190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 231544
19.4%
-10 122521
 
10.3%
-1 81188
 
6.8%
-2 69301
 
5.8%
-11 68735
 
5.8%
-15 62055
 
5.2%
-14 51958
 
4.4%
-20 46403
 
3.9%
-12 38186
 
3.2%
-60 35302
 
3.0%
Other values (184) 385564
32.3%
ValueCountFrequency (%)
-366.184 275
 
< 0.1%
-302.2 5565
 
0.5%
-299 7594
 
0.6%
-294 20491
1.7%
-156 19642
1.6%
-134 16180
1.4%
-120 4833
 
0.4%
-100 24
 
< 0.1%
-99.9 75
 
< 0.1%
-80 97
 
< 0.1%
ValueCountFrequency (%)
0 231544
19.4%
-0.074 736
 
0.1%
-0.08 102
 
< 0.1%
-0.088 203
 
< 0.1%
-0.1 11151
 
0.9%
-0.12 20
 
< 0.1%
-0.14 377
 
< 0.1%
-0.16 817
 
0.1%
-0.2 9509
 
0.8%
-0.24 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.9 MiB
False (F)
1050099 
True (T)
142658 

Length

Max length9
Median length9
Mean length8.8803964
Min length8

Characters and Unicode

Total characters10592155
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse (F)
2nd rowFalse (F)
3rd rowFalse (F)
4th rowFalse (F)
5th rowFalse (F)

Common Values

ValueCountFrequency (%)
False (F) 1050099
88.0%
True (T) 142658
 
12.0%

Length

2023-07-09T00:11:02.242700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:11:02.468981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
false 1050099
44.0%
f 1050099
44.0%
true 142658
 
6.0%
t 142658
 
6.0%

Most occurring characters

ValueCountFrequency (%)
F 2100198
19.8%
e 1192757
11.3%
1192757
11.3%
( 1192757
11.3%
) 1192757
11.3%
a 1050099
9.9%
l 1050099
9.9%
s 1050099
9.9%
T 285316
 
2.7%
r 142658
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4628370
43.7%
Uppercase Letter 2385514
22.5%
Space Separator 1192757
 
11.3%
Open Punctuation 1192757
 
11.3%
Close Punctuation 1192757
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1192757
25.8%
a 1050099
22.7%
l 1050099
22.7%
s 1050099
22.7%
r 142658
 
3.1%
u 142658
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
F 2100198
88.0%
T 285316
 
12.0%
Space Separator
ValueCountFrequency (%)
1192757
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1192757
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1192757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7013884
66.2%
Common 3578271
33.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 2100198
29.9%
e 1192757
17.0%
a 1050099
15.0%
l 1050099
15.0%
s 1050099
15.0%
T 285316
 
4.1%
r 142658
 
2.0%
u 142658
 
2.0%
Common
ValueCountFrequency (%)
1192757
33.3%
( 1192757
33.3%
) 1192757
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10592155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 2100198
19.8%
e 1192757
11.3%
1192757
11.3%
( 1192757
11.3%
) 1192757
11.3%
a 1050099
9.9%
l 1050099
9.9%
s 1050099
9.9%
T 285316
 
2.7%
r 142658
 
1.3%

Base TU Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.8 MiB
False (F)
941334 
True (T)
251423 

Length

Max length9
Median length9
Mean length8.7892085
Min length8

Characters and Unicode

Total characters10483390
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse (F)
2nd rowFalse (F)
3rd rowFalse (F)
4th rowFalse (F)
5th rowFalse (F)

Common Values

ValueCountFrequency (%)
False (F) 941334
78.9%
True (T) 251423
 
21.1%

Length

2023-07-09T00:11:02.656936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:11:02.875144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
false 941334
39.5%
f 941334
39.5%
true 251423
 
10.5%
t 251423
 
10.5%

Most occurring characters

ValueCountFrequency (%)
F 1882668
18.0%
e 1192757
11.4%
1192757
11.4%
( 1192757
11.4%
) 1192757
11.4%
a 941334
9.0%
l 941334
9.0%
s 941334
9.0%
T 502846
 
4.8%
r 251423
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4519605
43.1%
Uppercase Letter 2385514
22.8%
Space Separator 1192757
 
11.4%
Open Punctuation 1192757
 
11.4%
Close Punctuation 1192757
 
11.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1192757
26.4%
a 941334
20.8%
l 941334
20.8%
s 941334
20.8%
r 251423
 
5.6%
u 251423
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
F 1882668
78.9%
T 502846
 
21.1%
Space Separator
ValueCountFrequency (%)
1192757
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1192757
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1192757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6905119
65.9%
Common 3578271
34.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 1882668
27.3%
e 1192757
17.3%
a 941334
13.6%
l 941334
13.6%
s 941334
13.6%
T 502846
 
7.3%
r 251423
 
3.6%
u 251423
 
3.6%
Common
ValueCountFrequency (%)
1192757
33.3%
( 1192757
33.3%
) 1192757
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10483390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 1882668
18.0%
e 1192757
11.4%
1192757
11.4%
( 1192757
11.4%
) 1192757
11.4%
a 941334
9.0%
l 941334
9.0%
s 941334
9.0%
T 502846
 
4.8%
r 251423
 
2.4%

FPN Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.9 MiB
True (T)
1186889 
False (F)
 
5868

Length

Max length9
Median length8
Mean length8.0049197
Min length8

Characters and Unicode

Total characters9547924
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue (T)
2nd rowTrue (T)
3rd rowTrue (T)
4th rowTrue (T)
5th rowTrue (T)

Common Values

ValueCountFrequency (%)
True (T) 1186889
99.5%
False (F) 5868
 
0.5%

Length

2023-07-09T00:11:03.063045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-09T00:11:03.276927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
true 1186889
49.8%
t 1186889
49.8%
false 5868
 
0.2%
f 5868
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 2373778
24.9%
e 1192757
12.5%
1192757
12.5%
( 1192757
12.5%
) 1192757
12.5%
r 1186889
12.4%
u 1186889
12.4%
F 11736
 
0.1%
a 5868
 
0.1%
l 5868
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3584139
37.5%
Uppercase Letter 2385514
25.0%
Space Separator 1192757
 
12.5%
Open Punctuation 1192757
 
12.5%
Close Punctuation 1192757
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1192757
33.3%
r 1186889
33.1%
u 1186889
33.1%
a 5868
 
0.2%
l 5868
 
0.2%
s 5868
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
T 2373778
99.5%
F 11736
 
0.5%
Space Separator
ValueCountFrequency (%)
1192757
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1192757
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1192757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5969653
62.5%
Common 3578271
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2373778
39.8%
e 1192757
20.0%
r 1186889
19.9%
u 1186889
19.9%
F 11736
 
0.2%
a 5868
 
0.1%
l 5868
 
0.1%
s 5868
 
0.1%
Common
ValueCountFrequency (%)
1192757
33.3%
( 1192757
33.3%
) 1192757
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9547924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2373778
24.9%
e 1192757
12.5%
1192757
12.5%
( 1192757
12.5%
) 1192757
12.5%
r 1186889
12.4%
u 1186889
12.4%
F 11736
 
0.1%
a 5868
 
0.1%
l 5868
 
0.1%

Interactions

2023-07-09T00:10:11.538675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:27.661776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:31.535023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:35.459015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:39.298150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:43.173174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:47.179775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:51.148948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:55.142508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:59.373730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:03.406930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:07.402178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:11.892450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:28.020606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:31.866747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:35.774289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:39.619267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:43.513038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:47.512387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:51.483656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:55.476867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:59.691866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:03.744751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:07.737802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:12.237052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:28.346133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:32.211816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:36.092912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:39.947013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:43.841088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:47.848967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:51.819319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:55.841869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:00.039628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:04.087778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:08.101888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:12.585840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:28.672083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:32.563571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:36.410570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:40.251813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:44.166103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:48.176373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:52.156245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:56.175620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:00.376941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:04.422891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:08.440555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:12.918787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:28.982214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:32.896285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:36.739770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:40.574860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:44.500414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:48.511765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:52.482157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:56.542881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:00.707170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:04.742646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:08.783775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:13.257914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:29.289577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:33.211358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:37.054361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:40.898656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:44.822469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:48.825680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:52.818688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:56.873100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:01.055577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:05.053656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:09.135849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:13.586161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:29.600896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:33.520287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:37.375027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:41.212020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:45.169067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:49.152840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:53.130424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:57.216567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:01.383717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:05.399864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:09.482498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:13.920530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:29.920425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:33.833323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:37.681668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:41.515563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:45.488851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:49.477019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:53.455938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:57.559844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:01.715906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:05.735586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:09.834994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:14.260992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:30.258114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:34.158993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:38.005978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:41.844303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:45.853446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:49.824346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:53.798146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:57.922951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:02.043586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:06.086648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:10.184713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:14.600027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:30.561873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:34.467038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:38.315498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:42.160769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:46.175081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:50.153854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:54.132742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:58.272200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:02.368089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:06.394142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:10.520705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:14.934710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:30.874547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:34.778491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:38.624067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:42.469724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:46.500210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:50.473375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:54.455581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:58.627970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:02.696074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:06.712342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:10.839649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:15.284425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:31.202845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:35.122331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:38.954942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:42.823135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:46.829723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:50.810428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:54.811360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:09:59.003495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:03.050054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:07.045369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-09T00:10:11.179680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-09T00:11:03.552901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
settlementdate_monthsettlementdate_daysettlementperiodacceptedpriceacceptedvolumeLOC LATLOC LONGLOC Center LATLOC Center LONGTransmission Loss FactorGeneration CapacityDemand Capacityrecordtypesettlementdate_yearBMU TypeBMU Fuel TypeBMU GSP Group IdBMU GSP Group NameGSP LOC CenterBZONEBZONE GENERATIONBZONE DEMANDZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z11Z12Z13Z14Z15Z16Z17Trading UnitPC FlagPC StatusExempt Export FlagBase TU FlagFPN Flag
settlementdate_month1.0000.0160.0130.134-0.019-0.009-0.005-0.012-0.0140.024-0.0060.0190.1180.3070.0660.1020.0780.0780.0780.0800.1010.1010.0730.1650.0800.0810.0950.0440.0500.0620.0700.0570.0840.0570.0660.0390.0460.0570.0660.0840.0390.1130.0430.1050.063
settlementdate_day0.0161.000-0.0100.004-0.007-0.0110.005-0.0110.0060.0070.015-0.0090.0300.0470.0170.0350.0280.0280.0280.0290.0360.0360.0150.0450.0330.0260.0380.0160.0200.0230.0330.0220.0290.0250.0190.0120.0130.0190.0200.0310.0230.0290.0280.0240.014
settlementperiod0.013-0.0101.0000.0860.000-0.0400.016-0.0340.0180.034-0.0430.0140.0750.0250.0780.0990.0530.0530.0530.0550.0560.0560.0200.0420.0620.0340.0530.0440.0630.0530.0580.0400.0520.0350.0600.0320.0320.0170.0190.0820.0520.1590.0470.1550.047
acceptedprice0.1340.0040.0861.0000.564-0.5170.335-0.4980.4310.4940.143-0.1820.0480.0200.0110.0510.0300.0300.0300.0340.0160.0160.0060.0160.0090.0120.0090.0080.0010.0040.0140.0120.0040.0170.0060.0000.0300.0120.0010.1100.0080.0170.0100.0170.002
acceptedvolume-0.019-0.0070.0000.5641.000-0.2970.135-0.2830.2290.276-0.073-0.0280.5470.0900.1580.1680.1260.1260.1260.2270.1470.1470.4250.1600.0880.3100.1050.0760.0780.0970.0850.0770.1090.1440.1150.1120.1020.1330.1300.1710.1780.3210.2350.3440.048
LOC LAT-0.009-0.011-0.040-0.517-0.2971.000-0.4750.974-0.649-0.941-0.3690.3420.3910.1050.2830.4510.6490.6490.6490.7560.7440.7440.8620.9040.6890.5980.7040.6150.6510.5970.7300.6250.5960.6930.7620.6150.5070.7250.7710.6320.2450.4250.3210.4400.103
LOC LONG-0.0050.0050.0160.3350.135-0.4751.000-0.5220.8750.4810.402-0.1710.2410.0780.1650.3970.6020.6020.6020.6250.5880.5880.3250.5340.3600.4500.4150.4680.4820.6010.4380.6040.4870.6400.4450.6340.4750.5490.4260.6550.3010.1840.2890.1880.069
LOC Center LAT-0.012-0.011-0.034-0.498-0.2830.974-0.5221.000-0.660-0.943-0.3760.3280.4090.0970.2170.4081.0001.0001.0000.7970.8490.8490.4590.8540.6200.4770.6040.5260.8190.7250.8330.7510.7080.6040.6160.6060.5190.7530.7700.8200.1870.3210.2200.3330.073
LOC Center LONG-0.0140.0060.0180.4310.229-0.6490.875-0.6601.0000.6250.367-0.3000.3290.0850.1810.3681.0001.0001.0000.7570.7310.7310.2400.5710.2640.3150.3080.4960.7230.7390.5940.6890.6720.5730.5930.6140.8280.5830.5660.7840.1920.2900.2480.3080.088
Transmission Loss Factor0.0240.0070.0340.4940.276-0.9410.481-0.9430.6251.0000.370-0.3140.3750.4220.1550.3820.6640.6640.6640.6540.7220.7220.5140.8810.5480.4890.5680.5070.7410.6240.6200.5940.6920.6810.5220.5240.3190.6690.7450.5800.1740.2270.2090.1670.094
Generation Capacity-0.0060.015-0.0430.143-0.073-0.3690.402-0.3760.3670.3701.000-0.5390.1700.0840.2820.4500.4410.4410.4410.4960.4340.4340.5230.4490.2610.4300.2830.2880.4060.3970.3000.3090.4460.4710.3840.3420.2770.3050.3680.6790.3340.6270.4440.6230.085
Demand Capacity0.019-0.0090.014-0.182-0.0280.342-0.1710.328-0.300-0.314-0.5391.0000.1260.0750.0750.4350.3410.3410.3410.4180.2800.2800.0550.3010.4690.4340.0790.4680.3410.3250.1310.1800.3430.1870.0950.0750.0790.1210.1350.8620.0900.2830.1110.1470.025
recordtype0.1180.0300.0750.0480.5470.3910.2410.4090.3290.3750.1700.1261.0000.1150.2870.5140.4170.4170.4170.4090.3730.3730.1390.3060.1840.3020.1910.1710.1600.1850.2010.1730.2300.1480.2200.1600.1430.1370.0990.4480.1050.2650.0060.2300.078
settlementdate_year0.3070.0470.0250.0200.0900.1050.0780.0970.0850.4220.0840.0750.1151.0000.0440.1410.1090.1090.1090.1130.0840.0840.0340.0900.0430.0770.0380.0510.0410.0530.0400.0370.0610.0400.0540.0350.0460.0330.0310.1320.0430.0620.0220.0480.087
BMU Type0.0660.0170.0780.0110.1580.2830.1650.2170.1810.1550.2820.0750.2870.0441.0000.4330.2290.2290.2290.2840.1860.1860.0810.1700.1690.2040.1590.2320.1100.2180.1820.1700.0960.1420.2470.1780.1880.0880.1320.4820.5950.7990.8080.9560.197
BMU Fuel Type0.1020.0350.0990.0510.1680.4510.3970.4080.3680.3820.4500.4350.5140.1410.4331.0000.4480.4480.4480.5240.5140.5140.4110.7000.4400.4370.3880.3280.3430.3320.5230.5530.3920.4410.4220.2500.2300.3030.3310.7430.3550.7940.3990.7840.176
BMU GSP Group Id0.0780.0280.0530.0300.1260.6490.6021.0001.0000.6640.4410.3410.4170.1090.2290.4481.0001.0001.0000.7730.9100.9100.4590.8540.6210.4770.6050.5950.9100.8240.8690.9140.8570.7790.8210.8160.8350.9040.9230.8090.2230.3480.2610.3700.097
BMU GSP Group Name0.0780.0280.0530.0300.1260.6490.6021.0001.0000.6640.4410.3410.4170.1090.2290.4481.0001.0001.0000.7730.9100.9100.4590.8540.6210.4770.6050.5950.9100.8240.8690.9140.8570.7790.8210.8160.8350.9040.9230.8090.2230.3480.2610.3700.097
GSP LOC Center0.0780.0280.0530.0300.1260.6490.6021.0001.0000.6640.4410.3410.4170.1090.2290.4481.0001.0001.0000.7730.9100.9100.4590.8540.6210.4770.6050.5950.9100.8240.8690.9140.8570.7790.8210.8160.8350.9040.9230.8090.2230.3480.2610.3700.097
BZONE0.0800.0290.0550.0340.2270.7560.6250.7970.7570.6540.4960.4180.4090.1130.2840.5240.7730.7730.7731.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.6640.3260.4060.3550.4060.106
BZONE GENERATION0.1010.0360.0560.0160.1470.7440.5880.8490.7310.7220.4340.2800.3730.0840.1860.5140.9100.9100.9101.0001.0001.0000.3410.7270.3460.4040.4090.4870.7280.7720.6430.8220.8170.6090.6060.6770.5810.7070.7700.7940.1880.2160.2340.2100.066
BZONE DEMAND0.1010.0360.0560.0160.1470.7440.5880.8490.7310.7220.4340.2800.3730.0840.1860.5140.9100.9100.9101.0001.0001.0000.3410.7270.3460.4040.4090.4870.7280.7720.6430.8220.8170.6090.6060.6770.5810.7070.7700.7940.1880.2160.2340.2100.066
Z10.0730.0150.0200.0060.4250.8620.3250.4590.2400.5140.5230.0550.1390.0340.0810.4110.4590.4590.4591.0000.3410.3411.0001.0000.7081.0000.3580.1040.1400.1300.1320.1010.1590.1270.0840.0750.0640.0960.1050.2520.0330.0550.0360.0470.018
Z20.1650.0450.0420.0160.1600.9040.5340.8540.5710.8810.4490.3010.3060.0900.1700.7000.8540.8540.8541.0000.7270.7271.0001.0000.3981.0000.0950.2730.3620.3360.3370.2650.4050.3310.2230.2010.1730.2530.1990.6130.1660.0680.1640.0230.034
Z30.0800.0330.0620.0090.0880.6890.3600.6200.2640.5480.2610.4690.1840.0430.1690.4400.6210.6210.6211.0000.3460.3460.7080.3981.0000.7141.0000.7100.4320.1590.5840.1260.2360.1930.1300.0960.1010.1480.1640.5780.0600.1830.0840.0990.028
Z40.0810.0260.0340.0120.3100.5980.4500.4770.3150.4890.4300.4340.3020.0770.2040.4370.4770.4770.4771.0000.4040.4041.0001.0000.7141.0000.7140.7180.2800.2130.1860.1670.3140.2560.1730.1260.1330.1950.2170.5510.2640.1950.2830.1700.037
Z50.0950.0380.0530.0090.1050.7040.4150.6040.3080.5680.2830.0790.1910.0380.1590.3880.6050.6050.6051.0000.4090.4090.3580.0951.0000.7141.0000.7100.3290.1850.5900.1460.2250.1830.1230.1110.0950.1400.1560.4520.2190.0430.2130.1420.026
Z60.0440.0160.0440.0080.0760.6150.4680.5260.4960.5070.2880.4680.1710.0510.2320.3280.5950.5950.5951.0000.4870.4870.1040.2730.7100.7180.7101.0000.7100.8190.5810.1810.7190.2780.1870.1350.1430.2110.2340.6820.2390.2710.2380.2580.062
Z70.0500.0200.0630.0010.0780.6510.4820.8190.7230.7410.4060.3410.1600.0410.1100.3430.9100.9100.9101.0000.7280.7280.1400.3620.4320.2800.3290.7101.0000.7131.0000.5390.7520.3650.2490.2200.1910.2790.3100.7670.0740.0270.1030.0030.074
Z80.0620.0230.0530.0040.0970.5970.6010.7250.7390.6240.3970.3250.1850.0530.2180.3320.8240.8240.8241.0000.7720.7720.1300.3360.1590.2130.1850.8190.7131.0000.7160.1200.8050.6840.2320.1660.1770.2600.2870.7640.1280.2500.1780.2560.087
Z90.0700.0330.0580.0140.0850.7300.4380.8330.5940.6200.3000.1310.2010.0400.1820.5230.8690.8690.8691.0000.6430.6430.1320.3370.5840.1860.5900.5811.0000.7161.0000.8190.3670.3940.4130.1670.1770.2620.2880.8300.1010.2100.1360.2320.082
Z100.0570.0220.0400.0120.0770.6250.6040.7510.6890.5940.3090.1800.1730.0370.1700.5530.9140.9140.9141.0000.8220.8220.1010.2650.1260.1670.1460.1810.5390.1200.8191.0000.4120.2950.6040.5850.5060.2040.2260.8900.1220.2010.1340.2080.049
Z110.0840.0290.0520.0040.1090.5960.4870.7080.6720.6920.4460.3430.2300.0610.0960.3920.8570.8570.8571.0000.8170.8170.1590.4050.2360.3140.2250.7190.7520.8050.3670.4121.0000.5820.2990.3330.1760.4590.4550.7910.1010.0190.0930.0060.067
Z120.0570.0250.0350.0170.1440.6930.6400.6040.5730.6810.4710.1870.1480.0400.1420.4410.7790.7790.7791.0000.6090.6090.1270.3310.1930.2560.1830.2780.3650.6840.3940.2950.5821.0000.2880.3720.2750.5680.5320.8010.1050.1740.1230.1680.048
Z130.0660.0190.0600.0060.1150.7620.4450.6160.5930.5220.3840.0950.2200.0540.2470.4220.8210.8210.8211.0000.6060.6060.0840.2230.1300.1730.1230.1870.2490.2320.4130.6040.2990.2881.0000.8380.5780.1601.0000.6440.1300.2480.1430.2740.055
Z140.0390.0120.0320.0000.1120.6150.6340.6060.6140.5240.3420.0750.1600.0350.1780.2500.8160.8160.8161.0000.6770.6770.0750.2010.0960.1260.1110.1350.2200.1660.1670.5850.3330.3720.8381.0001.0000.5820.5180.6900.1050.2120.1070.2120.055
Z150.0460.0130.0320.0300.1020.5070.4750.5190.8280.3190.2770.0790.1430.0460.1880.2300.8350.8350.8351.0000.5810.5810.0640.1730.1010.1330.0950.1430.1910.1770.1770.5060.1760.2750.5781.0001.0000.4310.2890.4620.0450.1360.0310.1320.025
Z160.0570.0190.0170.0120.1330.7250.5490.7530.5830.6690.3050.1210.1370.0330.0880.3030.9040.9040.9041.0000.7070.7070.0960.2530.1480.1950.1400.2110.2790.2600.2620.2040.4590.5680.1600.5820.4311.0001.0000.8470.0750.0430.0100.0250.033
Z170.0660.0200.0190.0010.1300.7710.4260.7700.5660.7450.3680.1350.0990.0310.1320.3310.9230.9230.9231.0000.7700.7700.1050.1990.1640.2170.1560.2340.3100.2870.2880.2260.4550.5321.0000.5180.2891.0001.0000.8060.0510.0210.0300.0050.008
Trading Unit0.0840.0310.0820.1100.1710.6320.6550.8200.7840.5800.6790.8620.4480.1320.4820.7430.8090.8090.8090.6640.7940.7940.2520.6130.5780.5510.4520.6820.7670.7640.8300.8900.7910.8010.6440.6900.4620.8470.8061.0000.5000.8790.6451.0000.304
PC Flag0.0390.0230.0520.0080.1780.2450.3010.1870.1920.1740.3340.0900.1050.0430.5950.3550.2230.2230.2230.3260.1880.1880.0330.1660.0600.2640.2190.2390.0740.1280.1010.1220.1010.1050.1300.1050.0450.0750.0510.5001.0000.5740.9410.5130.028
PC Status0.1130.0290.1590.0170.3210.4250.1840.3210.2900.2270.6270.2830.2650.0620.7990.7940.3480.3480.3480.4060.2160.2160.0550.0680.1830.1950.0430.2710.0270.2500.2100.2010.0190.1740.2480.2120.1360.0430.0210.8790.5741.0000.3730.7890.135
Exempt Export Flag0.0430.0280.0470.0100.2350.3210.2890.2200.2480.2090.4440.1110.0060.0220.8080.3990.2610.2610.2610.3550.2340.2340.0360.1640.0840.2830.2130.2380.1030.1780.1360.1340.0930.1230.1430.1070.0310.0100.0300.6450.9410.3731.0000.5540.026
Base TU Flag0.1050.0240.1550.0170.3440.4400.1880.3330.3080.1670.6230.1470.2300.0480.9560.7840.3700.3700.3700.4060.2100.2100.0470.0230.0990.1700.1420.2580.0030.2560.2320.2080.0060.1680.2740.2120.1320.0250.0051.0000.5130.7890.5541.0000.136
FPN Flag0.0630.0140.0470.0020.0480.1030.0690.0730.0880.0940.0850.0250.0780.0870.1970.1760.0970.0970.0970.1060.0660.0660.0180.0340.0280.0370.0260.0620.0740.0870.0820.0490.0670.0480.0550.0550.0250.0330.0080.3040.0280.1350.0260.1361.000

Missing values

2023-07-09T00:10:20.494457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-09T00:10:27.876400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-09T00:10:39.645336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

recordtypesettlementdatesettlementdate_yearsettlementdate_monthsettlementdate_daysettlementperiodBMU IDacceptedpriceacceptedvolumeBMU TypeBMU Fuel TypeBMU GSP Group IdBMU GSP Group NameLOC LATLOC LONGGSP LOC CenterLOC Center LATLOC Center LONGBZONEBZONE GENERATIONBZONE DEMANDZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z11Z12Z13Z14Z15Z16Z17BMU Party IDBMU Party NameTrading UnitPC FlagPC StatusTransmission Loss FactorGeneration CapacityDemand CapacityExempt Export FlagBase TU FlagFPN Flag
0BID2021-01-012021111E_GYAR-137.0-18.366ECCGT_AEastern England52.5838341.733725Dalham52.2266490.519993Z12750040000.00.00.00.00.00.00.00.00.04200.01400.0NaN1400.04200.01400.00.00.0INNOGY01RWE Generation UK plcNon-TrUnitDynamicProduction (P)0.004895420.0-18.0False (F)False (F)True (T)
1BID2021-01-012021111E_SHOS-135.0-0.666ECCGT_JSouth Eastern England50.829511-0.229161Pembury51.1480060.327832Z1614000175000.00.00.00.00.00.00.00.00.00.00.00.02800.01400.01400.0NaN1400.0SPGEN01VPI Power LimitedNon-TrUnitDynamicProduction (P)0.012051436.0-16.0False (F)False (F)True (T)
2OFFER2021-01-012021111T_CARR-274.9248.334TCCGT_GNorth Western England53.436959-2.408212Carnforth54.127464-2.769975Z917500190000.00.00.00.00.02250.01400.03980.0NaN0.02800.00.00.00.00.00.00.0CARRINGTCarrington Power LtdNon-TrUnitDynamicProduction (P)-0.002334470.0-10.0False (F)False (F)True (T)
3BID2021-01-012021111T_CDCL-140.0-11.084TCCGT_BEast Midlands53.307421-0.786058Ab Kettleby52.800048-0.927751Z102000090000.00.00.00.00.00.00.04240.00.0NaN1400.04200.00.00.00.00.00.0EECLUniper UK LimitedNon-TrUnitDynamicProduction (P)0.005473445.0-12.0False (F)False (F)True (T)
4OFFER2021-01-012021111T_CDCL-171.9322.584TCCGT_BEast Midlands53.307421-0.786058Ab Kettleby52.800048-0.927751Z102000090000.00.00.00.00.00.00.04240.00.0NaN1400.04200.00.00.00.00.00.0EECLUniper UK LimitedNon-TrUnitDynamicProduction (P)0.005473445.0-12.0False (F)False (F)True (T)
5OFFER2021-01-012021111T_DAMC-172.0270.000TCCGT_JSouth Eastern England51.4256500.600555Pembury51.1480060.327832Z1514000175000.00.00.00.00.00.00.00.00.00.00.01400.00.06560.0NaN1400.00.0SPGEN01VPI Power LimitedNon-TrUnitDynamicProduction (P)0.012051820.0-12.0False (F)False (F)True (T)
6OFFER2021-01-012021111T_GRAI-671.9230.188TCCGT_JSouth Eastern England51.4435460.707775Pembury51.1480060.327832Z1514000175000.00.00.00.00.00.00.00.00.00.00.01400.00.06560.0NaN1400.00.0EECLUniper UK LimitedGRAIN PS TRADING UNITDynamicProduction (P)0.012051468.0-14.0False (F)False (F)True (T)
7OFFER2021-01-012021111T_GRAI-770.0230.000TCCGT_JSouth Eastern England51.4435460.707775Pembury51.1480060.327832Z1514000175000.00.00.00.00.00.00.00.00.00.00.01400.00.06560.0NaN1400.00.0EECLUniper UK LimitedGRAIN PS TRADING UNITDynamicProduction (P)0.012051468.0-14.0False (F)False (F)True (T)
8OFFER2021-01-012021111T_GRAI-871.9230.000TCCGT_JSouth Eastern England51.4435460.707775Pembury51.1480060.327832Z1514000175000.00.00.00.00.00.00.00.00.00.00.01400.00.06560.0NaN1400.00.0EECLUniper UK LimitedGRAIN PS TRADING UNITDynamicProduction (P)0.012051468.0-14.0False (F)False (F)True (T)
9OFFER2021-01-012021111T_MEDP-173.2340.000TCCGT_JSouth Eastern England51.4399280.690906Pembury51.1480060.327832Z1514000175000.00.00.00.00.00.00.00.00.00.00.01400.00.06560.0NaN1400.00.0MEDWAYMedway Power LtdNon-TrUnitDynamicProduction (P)0.012051734.0-30.0False (F)False (F)True (T)
recordtypesettlementdatesettlementdate_yearsettlementdate_monthsettlementdate_daysettlementperiodBMU IDacceptedpriceacceptedvolumeBMU TypeBMU Fuel TypeBMU GSP Group IdBMU GSP Group NameLOC LATLOC LONGGSP LOC CenterLOC Center LATLOC Center LONGBZONEBZONE GENERATIONBZONE DEMANDZ1Z2Z3Z4Z5Z6Z7Z8Z9Z10Z11Z12Z13Z14Z15Z16Z17BMU Party IDBMU Party NameTrading UnitPC FlagPC StatusTransmission Loss FactorGeneration CapacityDemand CapacityExempt Export FlagBase TU FlagFPN Flag
1192747OFFER2023-06-30202363048T_RATS-3175.00115.000TCOAL_BEast Midlands52.865393-1.255016Ab Kettleby52.800048-0.927751Z1117500190000.00.00.00.00.00.00.00.02800.01400.0NaN1400.01400.00.00.00.00.0EECLUniper UK LimitedRatcliffe PS Trading UnitDynamicProduction (P)0.003454504.00-16.000False (F)False (F)True (T)
1192748BID2023-06-30202363048T_SAKNW-1-75.00-25.845TWIND_NSouthern Scotland55.368624-4.042596Leadhills55.469264-3.736325Z61500050000.00.0770.00.03100.0NaN2800.00.02250.00.00.00.00.00.00.00.00.0SANDYWF1Sandy Knowe Wind Farm LtdNon-TrUnitDynamicProduction (P)-0.00969987.00-1.000False (F)False (F)True (T)
1192749BID2023-06-30202363048T_SANQW-1-81.00-1.271TWIND_NSouthern Scotland55.346087-4.025307Leadhills55.469264-3.736325Z61500050000.00.0770.00.03100.0NaN2800.00.02250.00.00.00.00.00.00.00.00.0SCWCLSANQUHAR CWCLNon-TrUnitDynamicProduction (P)-0.00969932.080.000False (F)False (F)True (T)
1192750OFFER2023-06-30202363048T_SPLN-1120.00167.500TCCGT_BEast Midlands52.805849-0.131242Ab Kettleby52.800048-0.927751Z102000090000.00.00.00.00.00.00.04240.00.0NaN1400.04200.00.00.00.00.00.0SPALSpalding Energy Company LtdNon-TrUnitDynamicProduction (P)0.003454950.00-10.000False (F)False (F)True (T)
1192751BID2023-06-30202363048T_TKNEW-1-79.33-35.017TWIND_BEast Midlands53.4800000.840000Ab Kettleby52.800048-0.927751Z102000090000.00.00.00.00.00.00.04240.00.0NaN1400.04200.00.00.00.00.00.0TKWFLTriton Knoll Offshore WindNon-TrUnitDynamicProduction (P)0.003454412.00-5.000False (F)False (F)True (T)
1192752BID2023-06-30202363048T_TKNWW-1-79.33-81.150TWIND_BEast Midlands53.4800000.840000Ab Kettleby52.800048-0.927751Z102000090000.00.00.00.00.00.00.04240.00.0NaN1400.04200.00.00.00.00.00.0TKWFLTriton Knoll Offshore WindNon-TrUnitDynamicProduction (P)0.003454412.00-5.770False (F)False (F)True (T)
1192753BID2023-06-30202363048T_TWSHW-1-75.00-2.261TWIND_NSouthern Scotland55.347448-3.880540Leadhills55.469264-3.736325Z61500050000.00.0770.00.03100.0NaN2800.00.02250.00.00.00.00.00.00.00.00.0TWENTYSHTwentyshilling LimitedNon-TrUnitDynamicProduction (P)-0.00969937.80-7.324False (F)False (F)True (T)
1192754BID2023-06-30202363048T_WDRGW-1-75.00-20.917TWIND_NSouthern Scotland55.274700-4.178600Leadhills55.469264-3.736325Z61500050000.00.0770.00.03100.0NaN2800.00.02250.00.00.00.00.00.00.00.00.0WINDYRIGWindy Rig Wind Farm LimitedNon-TrUnitDynamicProduction (P)-0.00969942.80-0.760False (F)False (F)True (T)
1192755BID2023-06-30202363048T_WISTW-2-80.00-1.875TWIND_NSouthern Scotland55.245642-4.307956Leadhills55.469264-3.736325Z61500050000.00.0770.00.03100.0NaN2800.00.02250.00.00.00.00.00.00.00.00.0BRWLTDBROCKLOCH RIG WINDFARM LTDNon-TrUnitDynamicProduction (P)-0.01078861.500.000False (F)False (F)True (T)
1192756BID2023-06-30202363048V__LFLEX00125.00-0.800VBATTERY_LSouth Western England50.735186-3.909674South Tawton50.735186-3.909674Z1714000175000.00.00.00.00.00.00.00.00.00.00.00.01400.00.00.01400.0NaNFLEXTRCYFlexitricity LimitedNon-TrUnitConsumption (C)Consumption (C)0.0153560.000.000False (F)False (F)True (T)